New Directions in Anonymization: Permutation Paradigm, Verifiability by Subjects and Intruders, Transparency to Users

New Directions in Anonymization: Permutation Paradigm, Verifiability by Subjects and Intruders, Transparency to Users

Josep Domingo-Ferrer and Krishnamurty Muralidhar Josep Domingo-Ferrer is with the UNESCO Chair in Data Privacy, Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Av. Països Catalans 26, E-43007 Tarragona, Catalonia, e-mail josep.domingo@urv.cat. Krishnamurty Muralidhar is with the Dept. of Marketing and Supply Chain Management, University of Oklahoma, 307 West Brooks, Adams Hall Room 10, Norman OK 73019-4007, USA, e-mail krishm@ou.edu.
Abstract

There are currently two approaches to anonymization: “utility first” (use an anonymization method with suitable utility features, then empirically evaluate the disclosure risk and, if necessary, reduce the risk by possibly sacrificing some utility) or “privacy first” (enforce a target privacy level via a privacy model, e.g., -anonymity or -differential privacy, without regard to utility). To get formal privacy guarantees, the second approach must be followed, but then data releases with no utility guarantees are obtained. Also, in general it is unclear how verifiable is anonymization by the data subject (how safely released is the record she has contributed?), what type of intruder is being considered (what does he know and want?) and how transparent is anonymization towards the data user (what is the user told about methods and parameters used?).

We show that, using a generally applicable reverse mapping transformation, any anonymization for microdata can be viewed as a permutation plus (perhaps) a small amount of noise; permutation is thus shown to be the essential principle underlying any anonymization of microdata, which allows giving simple utility and privacy metrics. From this permutation paradigm, a new privacy model naturally follows, which we call -permuted privacy. The privacy ensured by this method can be verified by each subject contributing an original record (subject-verifiability) and also at the data set level by the data protector. We then proceed to define a maximum-knowledge intruder model, which we argue should be the one considered in anonymization. Finally, we make the case for anonymization transparent to the data user, that is, compliant with Kerckhoff’s assumption (only the randomness used, if any, must stay secret).

Keywords: Data anonymization, statistical disclosure control, privacy, permutation paradigm, subject-verifiability, intervenability, intruder model, transparency to users.

1 Introduction

In the information society, public administrations and enterprises are increasingly collecting, exchanging and releasing large amounts of sensitive and heterogeneous information on individual subjects. Typically, a small fraction of these data is made available to the general public (open data) for the purposes of improving transparency, planning, business opportunities and general well-being. Other data sets are released only to scientists for research purposes, or exchanged among companies [4].

Privacy is a fundamental right included in Article 12 of the Universal Declaration of Human Rights. However, if privacy is understood as seclusion [30], it is hardly compatible with the information society and with current pervasive data collection. A more realistic notion of privacy in our time is informational self-determination. This right was mentioned for the first time in a German constitutional ruling dated 15 Dec. 1983 as “the capacity of the individual to determine in principle the disclosure and use of his/her personal data” and it also underlies the classical privacy definition by [31].

Privacy legislation in most developed countries forbids releasing and/or exchanging data that are linkable to individual subjects (re-identification disclosure) or allow inferences on individual subjects (attribute disclosure). Hence, in order to forestall any disclosure on individual subjects, data that are intended for release and/or exchange should first undergo a process of data anonymization, sanitization, or statistical disclosure control (e.g., see [14] for a reference work).

Statistical disclosure control (SDC) takes care of respondent/subject privacy by anonymizing three types of outputs: tabular data, interactive databases and microdata files. Microdata files consist of records each of which contains data about one individual subject (person, enterprise, etc.) and the other two types of output can be derived from microdata. Hence, we will focus on microdata. The usual setting in microdata SDC is for a data protector (often the same entity that owns and releases the data) to hold the original data set (with the original responses by the subjects) and modify it to reduce the disclosure risk. There are two approaches for disclosure risk control in SDC:

  • Utility first. An anonymization method with a heuristic parameter choice and with suitable utility preservation properties111It is very difficult, if not impossible, to assess utility preservation for all potential analyses that can be performed on the data. Hence, by utility preservation we mean preservation of some preselected target statistics (for example means, variances, correlations, classifications or even some model fitted to the original data that should be preserved by the anonymized data). is run on the microdata set and, after that, the risk of disclosure is measured. For instance, the risk of re-identification can be estimated empirically by attempting record linkage between the original and the anonymized data sets (see [29]), or analytically by using generic measures (e.g., [15]) or measures tailored to a specific anonymization method (e.g., [10] for sampling). If the extant risk is deemed too high, the anonymization method must be re-run with more privacy-stringent parameters and probably with more utility sacrifice.

  • Privacy first. In this case, a privacy model is enforced with a parameter that guarantees an upper bound on the re-identification disclosure risk and perhaps also on the attribute disclosure risk. Model enforcement is achieved by using a model-specific anonymization method with parameters that derive from the model parameters. Well-known privacy models include -differential privacy [8], -indistinguishability [9], -anonymity [24] and the extensions of the latter taking care of attribute disclosure, like -diversity [18], -closeness [16], -closeness [17], crowd-blending privacy [13] and others. If the utility of the resulting anonymized data is too low, then the privacy model in use should be enforced with a less strict privacy parameter or even replaced by a different privacy model.

1.1 Diversity of anonymization principles

Anonymization methods for microdata rely on a diversity of principles, and this makes it difficult to analytically compare their utility and data protection properties [7]; this is why one usually resorts to empirical comparisons [5]. A first high-level distinction is between data masking and synthetic data generation. Masking generates a modified version of the original data microdata set , and it can be perturbative masking ( is a perturbed version of the original microdata set ) or non-perturbative masking ( is obtained from by partial suppressions or reduction of detail, yet the data in are still true). Synthetic data are artificial (i.e. simulated) data that preserve some preselected properties of the original data . The vast majority of anonymization methods are global methods, in that a data protector with access to the full original data set applies the method and obtains the anonymized data set. There exist, however, local perturbation methods, in which the subjects do not need to trust anyone and can anonymize their own data (e.g., [22, 26]).

1.2 Shortcomings related to subjects, intruders and users

We argue that current anonymization practice does not take the informational self-determination of the subject into account. Since in most cases the data releaser is held legally responsible for the anonymization (for example, this happens in official statistics), the releaser favors global anonymization methods, where he can make all choices (methods, parameters, privacy and utility levels, etc.). When supplying their data, the subjects must hope there will be a data protector who will adequately protect their privacy in case of release. Whereas this hope may be reasonable for government surveys, it may be less so for private surveys (customer satisfaction surveys, loyalty program questionnaires, social network profiles, etc.). Indeed, a lot of privately collected data sets end up in the hands of data brokers [11], who trade with them with little or no anonymization. Hence, there is a fundamental mismatch between the kind of subject privacy (if any) offered by data releasers/protectors and privacy understood as informational self-determination.

The intruder model is also a thorny issue in anonymization. In the utility-first approach and in privacy models belonging to the -anonymity family, restrictive assumptions are made on the amount of background knowledge available to the intruder for re-identification. Assuming that a generic intruder knows this but not that is often rather arbitrary. In the -differential privacy model, no restrictions are placed on the intruder’s knowledge; the downside is that, to protect against re-identification by such an intruder, the original data set must be perturbed to an extent such that the presence or absence of any particular original record becomes unnoticeable in the anonymized data set. How to deal with an unrestricted intruder incurring as little utility damage as possible is an open issue.

Another unresolved debate is how much detail shall or can be given to the user on the masking methods and parameters used to anonymize a data release [2]. Whereas the user would derive increased inferential utility from learning as much as possible on how anonymization was performed, for some methods such details might result in disclosure of original data. Thus, even though Kerckhoff’s principle is considered a golden rule in data encryption (encryption and decryption algorithms must be public and the only secret parameter must be the key), it is still far from being achieved/accepted in data anonymization.

1.3 Contribution and plan of this paper

We first give in Section 2 a procedure that, for any anonymization method, allows mapping the anonymized attribute values back to the original attribute values, thereby preserving the marginal distributions of original attributes (reverse mapping).

Based on reverse mapping, we show in Section 3 that any anonymization method for microdata can be regarded as a permutation that may be supplemented by a small noise addition (permutation paradigm). Permutation is thus shown to be the essential principle underlying any anonymization of microdata, which allows giving simple utility and privacy metrics that can also be used to compare methods with each other.

From the permutation paradigm, a new privacy model naturally follows, which we present in Section 4 under the name -permuted privacy. Like all other privacy models, this model can be verified by the data protector for the entire original data set. A more attractive feature is that the subject contributing each original record can verify to what extent the privacy guarantee of the model holds for her record (subject-verifiability). Note that subject-verifiability is a major step towards informational self-determination, because it gives the subject control on how her data have been anonymized (a property that has also been called intervenability [23]).

Then in Section 5 we introduce a maximum-knowledge intruder model, which makes any assumptions about background knowledge unnecessary. We describe how such an intruder can optimally guess the correspondence between anonymized and original records and how he can assess the accuracy of his guess. Further, we show how to protect against such a powerful intruder by using anonymization methods that provide an adequate level of permutation.

Finally, in Section 6 we make the case for anonymization transparent to the data user. Just as Kerckhoff’s assumption is the guiding principle in data encryption, it should be adopted in anonymization: good anonymization methods should remain safe when everything (anonymized data, original data, anonymization method and parameters) except the anonymization key (randomness used) is published.

We illustrate all concepts introduced with a running example. Finally, conclusions and future research directions are gathered in Section 7.

2 Reverse mapping of anonymized data

We next recall a reverse-mapping procedure, which we first gave in the conference paper [20] in another context. Let the values taken by attribute in the original data set. Let represent the anonymized version of . We make no assumptions about the anonymization method used to generate , but we assume that the values in both and can be ranked in some way222 For numerical or categorical ordinal attributes, ranking is straightforward. Even for categorical nominal attributes, the ranking assumption is less restrictive than it appears, because semantic distance metrics are available that can be used to rank them (for instance, the marginality distance in [6, 27]).; any ties in them are broken randomly. Knowledge of and allows deriving another set of values via reverse mapping, as per Algorithm 1.

0:  Original attribute
0:  Anonymized attribute
  for  to  do
     Compute
     Set (where is the value of of rank )
  end for
  return  
Algorithm 1 Reverse-mapping conversion

Releasing the reverse-mapped attribute instead of has a number of advantages:

  • By construction, each reverse-mapped attribute preserves the rank correlation between the corresponding anonymized attribute and the rest of attributes in the data set; hence, reverse mapping does not damage the rank correlation structure of the original data set more than the underlying anonymization method.

  • In fact, incurs less information loss than since preserves the marginal distribution of the original attribute .

  • Disclosure risk can be conveniently measured by the rank order correlation between and (the higher, the more risk).

In Table 1 we give a running example. The original data set consists of three attributes , and which have been generated by sampling , and distributions, respectively. The masked data set consists of three attributes , and obtained, respectively, from , and by noise addition. The noise added to was sampled from a , the noise added to from a and the noise added to from a . The reverse-mapped attributes obtained using Algorithm 1 are , and , respectively.

In Table 1 we also give the ranks of values for the original and masked attributes, so that Algorithm 1 can be verified on the table. By way of illustration, consider the first attribute of the the first record. For the first original record, . This value turns out to be the 10th value of sorted in increasing order. After adding noise to , we get the masked value , which is the 14th value of sorted in increasing order. Then, to do the reverse mapping, we replace by the 14th value of (108.21) and we get .

Clearly the values of each are a permutation of the values of the corresponding , for . Hence, the reverse-mapped attributes preserve the marginal distribution of the corresponding original attributes. The disclosure risk can be measured by the rank correlations between and (0.722), between and (0.844) and between and (0.776).

  • 103.69 981.80 4928.80 10 8 8 108.18 972.62 4876.73 14 7 5 108.21 980.97 4893.50
    93.13 980.97 4931.16 2 7 9 96.60 1020.73 5005.04 6 11 13 96.18 988.44 4986.25
    100.87 902.21 5108.54 9 1 15 105.26 882.92 4900.68 13 1 7 107.62 902.21 4905.71
    95.24 953.37 5084.18 4 4 14 88.02 944.54 4949.78 2 4 10 93.13 953.37 4941.81
    96.18 1086.34 5212.25 6 20 18 91.57 1057.83 5267.57 5 18 19 95.50 1052.34 5232.96
    93.16 986.70 5232.96 3 10 19 100.41 991.34 5230.64 8 9 18 99.72 984.87 5212.25
    95.50 952.13 4824.95 5 3 3 100.31 959.89 4824.03 7 5 4 98.99 971.09 4835.05
    115.53 988.44 5437.43 19 11 20 123.37 1061.23 5450.70 20 19 20 116.75 1057.63 5437.43
    98.99 941.48 4835.05 7 2 4 103.12 903.25 4752.03 10 2 3 103.69 941.48 4824.95
    109.96 984.87 4950.48 16 9 11 104.82 912.77 4997.61 12 3 12 105.59 952.13 4954.28
    99.72 1005.19 5158.64 8 13 17 87.83 1025.01 5166.63 1 12 17 87.62 990.58 5158.64
    116.75 1057.63 4986.25 20 19 13 112.21 1082.43 4988.44 15 20 11 109.81 1086.34 4950.48
    107.62 1025.13 4954.28 13 15 12 114.29 988.93 4889.75 17 8 6 110.63 981.80 4900.79
    87.62 1031.74 4905.71 1 17 7 90.83 1049.58 4902.04 4 15 8 95.24 1025.13 4928.80
    109.81 971.09 4941.81 15 5 10 113.64 1002.19 5020.71 16 10 14 109.96 986.70 5084.18
    110.63 1052.34 4495.19 17 18 1 103.07 1052.03 4519.26 9 17 1 100.87 1031.74 4495.19
    113.76 972.20 4893.50 18 6 5 117.00 962.84 5087.90 19 6 16 115.53 972.20 5143.05
    105.59 1027.64 5143.05 12 16 16 89.43 1049.97 5072.79 3 16 15 93.16 1027.64 5108.54
    108.21 990.58 4714.76 14 12 2 115.79 1036.10 4662.73 18 13 2 113.76 1005.19 4714.76
    104.74 1023.96 4900.79 11 14 6 104.00 1037.00 4931.99 11 14 9 104.74 1023.96 4931.16
Table 1: Running example. Original data set, formed by attributes , and . Masked data set, formed by attributes , and obtained via noise addition. Reverse-mapped data set, formed by attributes , and . The notation stands for the ranks of the values of . Analogously for .

3 A permutation paradigm of anonymization

Reverse mapping has the following broader conceptual implication: any anonymization method is functionally equivalent to a two-step procedure consisting of a permutation step (mapping the original data set to the output of the reverse mapping procedure in Algorithm 1) plus a noise addition step (adding the difference between the reverse-mapped output and the anonymized data set).

Specifically, take to be the original data set, the anonymized data set and the reverse-mapped data set (the values of each attribute in are a permutation of the corresponding attribute in ). Now, conceptually, any anonymization method is functionally equivalent to doing the following: i) permute the original data set to obtain ; ii) add some noise to to obtain . The noise used to transform into is necessarily small (residual) because it cannot change any rank: note that, by the construction of Algorithm 1, the ranks of corresponding values of and are the same.

Let us emphasize that the functional equivalence described in the previous paragraph does not imply any actual change in the anonymization method: we are simply saying that the way the method transforms into could be exactly mimicked by first permuting and then adding residual noise.

In this light, it seems rather obvious that protection against re-identification via record linkage comes from the permutation step in the above functional equivalence: as justified above, the noise addition step in the equivalence does not change any ranks, so any rank change must come from the permutation step. Thus, any two anonymization methods can, however different their actual operating principles, be compared in terms of how much permutation they achieve, that is, how much they modify ranks.

On the other hand, to permute, one must have access to the full data set or at least a part of it. Hence, local perturbation methods, which operate locally by adding noise to each record, cannot guarantee a prescribed permutation amount; if they protect against re-identification, it is by means of “blind” noise addition, which may be an overkill.

We illustrate the view of anonymization as permutation plus residual noise on the running example (Table 2). First we permute each original attribute to obtain the corresponding , for . Then we add the noise required to obtain from the corresponding , for . It can be observed that, for , in general the values of are substantially smaller than those of where is the noise required to obtain directly from for .

  • 103.69 981.80 4928.80 108.21 980.97 4893.50 -0.03 -8.35 -16.77 108.18 972.62 4876.73 4.49 -9.18 -52.07
    93.13 980.97 4931.16 96.18 988.44 4986.25 0.42 32.29 18.79 96.60 1020.73 5005.04 3.47 39.76 73.88
    100.87 902.21 5108.54 107.62 902.21 4905.71 -2.36 -19.29 -5.03 105.26 882.92 4900.68 4.39 -19.29 -207.86
    95.24 953.37 5084.18 93.13 953.37 4941.81 -5.11 -8.83 7.97 88.02 944.54 4949.78 -7.22 -8.83 -134.40
    96.18 1086.34 5212.25 95.50 1052.34 5232.96 -3.93 5.49 34.61 91.57 1057.83 5267.57 -4.61 -28.51 55.32
    93.16 986.70 5232.96 99.72 984.87 5212.25 0.69 6.47 18.39 100.41 991.34 5230.64 7.25 4.64 -2.32
    95.50 952.13 4824.95 98.99 971.09 4835.05 1.32 -11.20 -11.02 100.31 959.89 4824.03 4.81 7.76 -0.92
    115.53 988.44 5437.43 116.75 1057.63 5437.43 6.62 3.60 13.27 123.37 1061.23 5450.70 7.84 72.79 13.27
    98.99 941.48 4835.05 103.69 941.48 4824.95 -0.57 -38.23 -72.92 103.12 903.25 4752.03 4.13 -38.23 -83.02
    109.96 984.87 4950.48 105.59 952.13 4954.28 -0.77 -39.36 43.33 104.82 912.77 4997.61 -5.14 -72.10 47.13
    99.72 1005.19 5158.64 87.62 990.58 5158.64 0.21 34.43 7.99 87.83 1025.01 5166.63 -11.89 19.82 7.99
    116.75 1057.63 4986.25 109.81 1086.34 4950.48 2.40 -3.91 37.96 112.21 1082.43 4988.44 -4.54 24.80 2.19
    107.62 1025.13 4954.28 110.63 981.80 4900.79 3.66 7.13 -11.04 114.29 988.93 4889.75 6.67 -36.20 -64.53
    87.62 1031.74 4905.71 95.24 1025.13 4928.80 -4.41 24.45 -26.76 90.83 1049.58 4902.04 3.21 17.84 -3.67
    109.81 971.09 4941.81 109.96 986.70 5084.18 3.68 15.49 -63.47 113.64 1002.19 5020.71 3.83 31.10 78.90
    110.63 1052.34 4495.19 100.87 1031.74 4495.19 2.20 20.29 24.07 103.07 1052.03 4519.26 -7.56 -0.31 24.07
    113.76 972.20 4893.50 115.53 972.20 5143.05 1.47 -9.36 -55.15 117.00 962.84 5087.90 3.24 -9.36 194.40
    105.59 1027.64 5143.05 93.16 1027.64 5108.54 -3.73 22.33 -35.75 89.43 1049.97 5072.79 -16.16 22.33 -70.26
    108.21 990.58 4714.76 113.76 1005.19 4714.76 2.03 30.91 -52.03 115.79 1036.10 4662.73 7.58 45.52 -52.03
    104.74 1023.96 4900.79 104.74 1023.96 4931.16 -0.74 13.04 0.83 104.00 1037.00 4931.99 -0.74 13.04 31.20
Table 2: Running example. View of masking as permutation plus (small) noise. Original attributes are permuted to get , for . Then noise is added to to get . In general, for , less noise is required to obtain from than directly from (compare the absolute values of columns and ).

4 A new subject-verifiable privacy model: -permuted privacy

In Section 3, we have argued that permutation can be regarded as the essential principle of microdata anonymization. This suggests adopting a new privacy model focusing on permutation. Note that no privacy model in the literature considers permutation. Our proposal follows.

Definition 1 (-permuted privacy w.r.t. a record)

Given a non-negative integer and an -dimensional vector of non-negative real numbers, an anonymized data set with attributes is said to satisfy -permuted privacy with respect to original record if,

  1. The permutation distance for is at least in the following sense: given the anonymized attribute values closest to the respective attribute values of , no anonymized record exists such that the ranks of and differ less than for all .

  2. For , if is the value of the anonymized -th attribute closest to the value of the -th attribute of , and is the set of values of the sorted whose rank differs no more than from ’s rank, then the variance of is greater than the -th component of .

Definition 2 (-permuted privacy for a data set)

An anonymized data set is said to satisfy -permuted privacy if it satisfies -permuted privacy with respect to all records in the original data set.

The permutation distance mentioned in the first condition of Definition 1 can be computed using Algorithm 2. A few comments on this algorithm follow:

  • For every attribute , the algorithm first determines the anonymized value closest to the original value of the subject. If the anonymization is just a permutation without noise addition (either because the method used involves only permutation or because the anonymized data set has been reverse-mapped with Algorithm 1 using knowledge of the original data set), then .

  • The goal is to determine whether these most similar values for have been permuted far enough from each other in terms of ranks.

The condition on variances in Definition 1 ensures that there is enough diversity, similar to what -diversity [18] adds to -anonymity. Variances of non-numerical attributes can be computed as described in [6].

0:   {Original record containing attribute values}
0:   {Anonymized data set containing records with attributes }
  for  to  do
     Let be the value of closest to
     Sort by
     Let be the rank (record no.) of in the sorted
     for  to  do
        Let be the rank of in the sorted
     end for
  end for
  Set
  while  such that it holds that  do
     
  end while
  return  permutation distance
Algorithm 2 Permutation distance computation for an original record

Let us give a numerical illustration of how Algorithm 2 works. Assume that one wants to determine the permutation distance for the third original record of Table 2, that is, . The algorithm looks for the values of , and closest to , and , respectively. These are , and , shown in boxes in Table 3. The ranks of these values are , and (the reader can find them boxed in colums , and of Table 3). Then the algorithm looks for the record whose attribute ranks deviate minimally from (the rank deviations are shown in columns , and of Table 3). This record turns out to be the 10th anonymized record (shown in underlined boldface in Table 3) and its rank deviations from the anonymized attribute values closest to the original attribute values are 4, 1, 4, respectively. Hence, the permutation distance for the third original record is .

Regarding the condition on variances in Definition 1, we can compute the variances of the three anonymized attributes restricted to the sets , and , respectively. For example

and the variance of the values of is 24.70. Similarly, for and the corresponding variances are 896.76 and 20167.78, respectively. Hence, the anonymized data set in Table 3 satisfies -permuted privacy with respect to the third original record, with and .

  • 108.18 972.62 4876.73 14 7 5 6 5 11 11
    96.60 1020.73 5005.04 6 11 13 2 9 3 9
    105.26 882.92 4900.68 13 1 7 5 1 9 9
    88.02 944.54 4949.78 2 4 10 6 2 6 6
    91.57 1057.83 5267.57 5 18 19 3 16 3 16
    100.41 991.34 5230.64 8 9 18 0 7 2 7
    100.31 959.89 4824.03 7 5 4 1 3 12 12
    123.37 1061.23 5450.70 20 19 20 12 17 4 17
    103.12 903.25 4752.03 10 2 3 2 0 13 13
    104.82 912.77 4997.61 12 3 12 4 1 4 4
    87.83 1025.01 5166.63 1 12 17 7 10 1 10
    112.21 1082.43 4988.44 15 20 11 7 18 5 18
    114.29 988.93 4889.75 17 8 6 9 6 10 10
    90.83 1049.58 4902.04 4 15 8 4 13 8 13
    113.64 1002.19 5020.71 16 10 14 8 8 2 8
    103.07 1052.03 4519.26 9 17 1 1 15 15 15
    117.00 962.84 5087.90 19 6 16 11 4 0 11
    89.43 1049.97 5072.79 3 16 15 5 14 1 14
    115.79 1036.10 4662.73 18 13 2 10 11 14 14
    104.00 1037.00 4931.99 11 14 9 3 12 7 12
Table 3: Running example. Computation of the permutation distance for the third original record of Table 2. The permutation distance is .

We can perform the above computations not only for the third original record, but for the entire original data set. We show the results in Table 4, which gives:

  • For each original record , the closest anonymized record and the permutation distance;

  • The data set-level permutation distance (this is the minimum of the record-level permutation distances);

  • For each original record and each anonymized attribute , the variance of and (between parentheses) the variance of , where is the data set-level permutation distance () and is the record-level permutation distance;

  • For each anonymized attribute, the data set-level minimum variance of the attribute values whose rank deviates no more than from the corresponding attribute value of the anonymized record closest to the original record (this is the minimum of the variances of ).

From the data-set level permutation distance and data-set level attribute variances, it can be seen that the anonymized data set satisfies -permuted privacy with and .

  • Rec #
    1 103.69 981.80 4928.80 108.18 972.62 4876.73 1 4 0.48 (12.45) 69.14 (682.15) 388.07 (2170.53)
    2 93.13 980.97 4931.16 96.60 1020.73 5005.04 2 4 6.57 (31.12) 69.14 (682.15) 388.07 (2170.53)
    3 100.87 902.21 5108.54 105.26 882.92 4900.68 10 4 1.63 (24.70) 155.00 (896.76) 1692.52 (20167.78)
    4 95.24 953.37 5084.18 88.02 944.54 4949.78 6 4 12.83 (32.47) 64.36 (1332.66) 1692.52 (20167.78)
    5 96.18 1086.34 5212.25 91.57 1057.83 5267.57 5 2 12.83 (16.94) 112.36 (118.46) 1738.89 (14751.48)
    6 93.16 986.70 5232.96 100.41 991.34 5230.64 6 3 6.57 (22.88) 69.14 (414.40) 1738.99(16208.14)
    7 95.50 952.13 4824.95 100.31 959.89 4824.03 7 1 12.83 (12.83) 385.03 (385.03) 2612.38 (2612.38)
    8 115.53 988.44 5437.43 123.37 1061.23 5450.70 17 4 1.23 (18.76) 69.14 (682.15) 8384.15 (2257.38)
    9 98.99 941.48 4835.05 103.12 903.25 4752.03 7 1 3.14 (3.14) 385.03 (385.03) 2612.38 (3407.82)
    10 109.96 984.87 4950.48 104.82 912.77 4997.61 13 4 8.12 (21.96) 69.14 (682.15) 555.30 (2952.80)
    11 99.72 1005.19 5158.64 87.83 1025.01 5166.63 6 1 3.14 (3.14) 147.25 (147.25) 3407.82 (1676.76)
    12 116.75 1057.63 4986.25 112.21 1082.43 4988.44 12 4 11.06 (13.03) 14.43 (169.01) 429.60 (1313.97)
    13 107.62 1025.13 4954.28 114.29 988.93 4889.75 20 3 8.12 (16.70) 41.95 (359.80) 555.30 (2257.38)
    14 87.62 1031.74 4905.71 90.83 1049.58 4902.04 14 3 0.01 (1.47) 29.73 (248.09) 208.80 (15949.39
    15 109.81 971.09 4941.81 113.64 1002.19 5020.71 10 4 8.12 (21.96) 115.82 (937.01) 555.30 (95.84)
    16 110.63 1052.34 4495.19 103.07 1052.03 4519.2 19 4 5.34 (22.74) 11.07 (190.00) 5145.91 (18406.42)
    17 113.76 972.20 4893.50 117.00 962.84 5087.90 13 1 0.75 (0.75) 115.82 (115.82) 95.84 (95.84)
    18 105.59 1027.64 5143.05 89.43 1049.97 5072.79 15 3 2.22 (14.82) 41.95 (359.80) 3407.82 (18406.42)
    19 108.21 990.58 4714.76 115.79 1036.10 4662.73 1 2 8.12 (12.76) 33.26 (252.94) 4352.91 (15949.39)
    20 104.74 1023.96 4900.79 104.00 1037.00 4931.99 20 2 0.27 (2.94) 41.95 (160.52) 30.26 (334.85)
    Data set-level permutation distance and variances 1 0.01 11.07 30.26
Table 4: Running example. Minimum permutation distances, for each original record ( for record ) and at the data set level (). Closest anonymized record to each original record (record no. is given in column ). Anonymized attribute variances within the data set-level minimum permutation distance , for each original record (record rows) and at the data set level (bottom row). Between parentheses, anonymized attribute variances for each original record within the record-level permutation distance (rather than ). The anonymized data set turns out to satisfy -permuted privacy with and .

Obviously, the data protector, who has access to the entire original data set and the entire anonymized data set, can verify as described in this section whether the anonymized data set satisfies -permuted privacy for any and of his choice. The most interesting feature, however, is that each subject can check whether -permuted privacy with respect to her original record is satisfied by the anonymized data set for some and of her choice. The subject only needs to know her original record and the anonymized data set: for example, the subject having contributed the third original record can compute the permutation distance as described in Table 3 and also compute variances of any subset of anonymized attribute values.

5 Intruder model in anonymization

There are some fundamental differences between data encryption and data anonymization: whereas the receiver of the encrypted data has the key to decrypt the ciphertext back to plaintext, the user in anonymization has access only to what plays the role of the ciphertext, that is, the anonymized data. Consequently, while it makes sense to release encrypted data that disclose absolutely nothing about the underlying plaintext (perfect secrecy, [25]), it does not make sense to release anonymized data that disclose absolutely nothing about the underlying original data. The objective of microdata release is to provide information to the public, which means that some disclosure is inherently inevitable. Even if data are anonymized prior to release, disclosure is still inevitable, because zero disclosure happens if and only if the anonymized data are completely useless, which makes the data release operation completely absurd. In fact, the privacy-first approach to data anonymization runs this risk of absurdity when too stringent privacy parameters are selected and enforced.

Another issue that complicates matters is that any user of anonymized data could, potentially, be also an intruder. Hence, modeling the intruder in anonymization is difficult since we have to consider many potential levels of intruder’s knowledge. Fortunately, and in spite of the aforementioned fundamental differences, data encryption does offer some principles that remain useful to tackle this characterization.

In cryptography, several different attack scenarios are distinguished depending on the intruder’s knowledge: ciphertext-only (the intruder only sees the ciphertext), known-plaintext (the intruder has access to one or more pairs of plaintext-ciphertext), chosen-plaintext (the intruder can choose any plaintext and observe the corresponding ciphertext), chosen-ciphertext (the intruder can choose any ciphertext and observe the corresponding plaintext).

In anonymization, we can equate the original data set to a plaintext and the anonymized data set to a ciphertext. Hence, a ciphertext-only attack would be one in which the intruder has access only to the anonymized data: this class of attacks can be dangerous, as shown by [28] for de-identified DNA data, by [21] for Netflix data and by [1] for the AOL data. Even if potentially dangerous, assuming that the intruder only knows the anonymized data can be naïve in some situations. For example, if the intruder is one of the subjects in the data set, he will normally know his own original record.

On the other hand, the strongest attacks in cryptography, namely chosen-plaintext and chosen-ciphertext attacks, assume some interaction between the intruder and the encryption system. Thus, they are not relevant in a non-interactive anonymization setting such as the one we are considering (release of anonymized data sets).

Hence, the strongest attack that anonymization of data sets must face is the known-plaintext attack. In this attack, one might think of an intruder knowing particular original records and their corresponding anonymized versions; however, this is unlikely, because anonymization precisely breaks the links between anonymized records and corresponding original records. A more reasonable definition for a known-plaintext attack in anonymization is the following.

Definition 3 (Known-plaintext attack in anonymization)

An attack of this class is one in which the intruder knows the entire original data set (plaintext) and the entire corresponding anonymized data set (ciphertext), his objective being to recreate the correct linkage between the original and the anonymized records.

We observe that our definition of the intruder is stronger than any other prior such definition in the data set anonymization scenario. One of the key issues in modeling the intruder in this context is to define his prior knowledge, including available background knowledge. As mentioned above, we assume that the intruder has maximum knowledge: he knows and , from which he can recreate by reverse mapping; hence, he only lacks the key, that is, the correct linkage between and . In particular, assuming knowledge of by the intruder eliminates the need to consider the presence/absence of external background knowledge (typically external identified data sets linkable through quasi-identifiers) when evaluating the ability of the intruder to disclose information. In this respect, the intruder’s background knowledge is as irrelevant in our intruder model as it is in -differential privacy.

As hinted above, knowledge of allows our intruder to reverse-map to , even if the data administrator only releases . Hence, using the permutation paradigm of Section 3, we can say that the intruder is able to remove the noise addition step in the functional equivalence of anonymization, so that he is only confronted with permutation. In other words, if we consider that noise addition is governed by one key (the random seed for the noise) and permutation by another key (the random seed for the permutation), reverse mapping allows the intruder to get rid of the former key and focus on the latter.

5.1 Record linkage computation by the intruder

The concept of record linkage has a long history in the disclosure limitation literature. Many different record linkage procedures have been suggested and two of the main procedures are distance-based record linkage and probabilistic record linkage (see [29] for a discussion on them). Yet, one of the key aspects affecting the success of record linkage is knowledge of the underlying procedure used to anonymize the data. For example, if normally distributed noise is used to mask the original data, then it has been shown that an optimal distance-based record linkage can be performed [12]. But in other cases, it cannot be shown that any particular record linkage method performs optimally. This results from the simple fact that record linkage must be able to reverse the anonymization procedure and, with a host of different anonymization procedures, this is a challenging task.

From the perspective of our intruder, however, all anonymization procedures are reduced to permutations of the original data. Thus, the best option to guess which original record corresponds to which permuted record is to use the above described permutation distance computation algorithm with the small adaptation of replacing the anonymized data set by the permuted data set in Algorithm 2. Note that we do not preclude the intruder from using some other record linkage procedure and using Algorithm 2 for purposes of confirmation.

For the sake of illustration, Table 5 shows the linkages our intruder would obtain when using Algorithm 2 on the and data sets of our running example. The following remarks are in order:

  • For some records in (record nos. 1, 9, 11 and 19), multiple matches are obtained. For example, for the first original record, both the first and the seventh permuted records are at shortest permutation distance.

  • Some records in (record nos. 4, 7, 10, 12 and 13) are matches to multiple records in , whereas some records in (record nos. 3, 8, 16 and 18) are matches to no record in .

The intruder can realize the above, which diminishes his confidence in the accuracy of the re-identification process.

Furthermore, it can be seen in Table 5 that 5 records are correctly linked, 4 records have multiple matches and the remaining 11 records are misidentified. While the data protector can realize this, the intruder cannot tell with certainty correct linkages from misidentifications, because he does not know the correct linkages. The data protector may use the proportion of correct linkages as a metric to evaluate the protection provided by anonymization.

1 1, 7 4
2 4 3
3 10 3
4 4 4
5 5 2
6 11 2
7 7 2
8 17 5
9 7, 9 3
10 15 3
11 2, 6 4
12 12 5
13 20 3
14 14 3
15 10 3
16 19 5
17 13 2
18 12 5
19 13, 19 4
20 20 3
Table 5: Running example. Record linkages computed by the intruder. For each original record in (record no. specified in column ), permuted records in at shortest permutation distance (record no. (or nos.) specified in column ). Both for and , the values of record no. can be found in the -th row of Table 2.

5.2 Record linkage verification by the intruder

The inability of an intruder to assess the accuracy of re-identification via record linkage is often viewed as providing plausible deniability to the data protector. In other words, even if the intruder boasts the record linkages he has computed (something like Table 5), he cannot prove with certainty which linkages are correct. Hence, any subject seeing that she has been correctly re-identified by the intruder (e.g., the subject behind original record no. 4 in Table 5) could be reassured by the data protector that re-identification has occurred by chance alone without the intruder really being sure about it.

However, an intruder with the knowledge specified in Definition 3 can perform the analysis described in this section to verify how likely it is for his computed record linkages to be correct. To do this, the intruder simply needs to generate a random set of values by drawing from the original data and then determine the permutation distance at which a match occurs from this random data.

For instance, assume that the intruder randomly draws one value from , another value from (independent of the draw from ), and a third value from (independent of the draws from and ). Assume that the first draw yields the value of in the fifth original record, the second draw the value of in the 19-th original record and the third draw the value of in the 10-th original record. The synthetic record formed by the intruder is . This record does not exist in the original data set . But even for this synthetic record there is some permutation distance at which the intruder is likely to find a matching record in Z. When the permutation distance computation algorithm is used for this synthetic record, a match is found at distance 2 and the matched record in is the second record (see the records of in Table 2).

Given that the size of our running example is small, the intruder can perform the above analysis (computing the permutation distance of the match in ) for all possible () records resulting from three random draws. Let be the data set containing these possible records. Within , 20 records are the original records in , 20 the permuted records in , and the remaining are actual synthetic records. Hence, for the 20 records in , a match in would be found at a permutation distance of zero. Table 6 shows the distribution of the permutation distance for the 20 original records in and for the possible records in . Figure 1 is a graphical representation of both distributions.

Distance Frequency Frequency
for for
0 0.0000 0.0025
1 0.0000 0.0586
2 0.2000 0.1899
3 0.4000 0.3014
4 0.2000 0.2595
5 0.2000 0.1288
6 0.0000 0.0428
7 0.0000 0.0143
8 0.0000 0.0024
9 0.0000 0.0000
10 0.0000 0.0000
Table 6: Running example. Distributions of the permutation distance of the match in for the set of 20 original records and for the set of the possible records that can be obtained by respective random draws from , and .
Figure 1: Running example. Graphical representation of the distributions of the permutation distance of the match in for the set of 20 original records and for the set of the possible records that can be obtained by respective random draws from , and .

Both Table 6 and Figure 1 highlight that the probability of finding a match at a particular permutation distance for an original record in is quite similar to the probability of finding a match at the same permutation distance for a random record in . Otherwise put, when a matching record is found for an original record in , there is a high probability that the match occurred by chance alone. Hence, upon seeing Table 6 and/or Figure 1, the intruder realizes that he cannot claim success in his record linkages because they are not reliable. In conclusion, the anonymization withstands a known-plaintext attack as per Definition 3. And, since the intruder of Definition 3 has maximum knowledge, the anonymized data are also safe from record linkage by any other intruder.

As Figure 1 illustrates, for a very small data set (such as the one in our running example), even a small level of permutation is likely to prevent the intruder from claiming success with re-identification. Note that random matches occur already at distances 0, 1, 2, etc., so short-distance matches actually due to small anonymization permutation are plausible as random matches. This need not be the case with larger data sets: if the number of records or the number of attributes are greater, then random matches at short distances may be extremely rare or even non-existing, in which case short-distance matches due to small permutation are no longer plausible as random matches. Hence, anonymizing with a small level of permutation may not suffice for larger data sets.

For the sake of illustration, consider an original data set with records randomly generated in the same way as the original data set in our running example. We first use perturbation through additive noise with the same characteristics as the one used in our running example and we get an anonymized data set . Then the intruder reverse-maps to get . While it would still be feasible to generate all potential combinations of values from (), for purposes of computational efficiency, we assume the intruder generates a data set with synthetic records by randomly and independently selecting values from attributes , and . Figure 2 depicts the distributions of the permutation distance for the original records (in ) and for the random records (in ). It turns out that both distributions are practically indistinguishable. So, we are in a similar situation as in Figure 1, although the permutation distances are much larger in Figure 2. Hence, if the intruder were to find a match, there is a high probability that the match could have occurred at random. We can conclude that the anonymization procedure used to obtain withstands a known-plaintext attack.

Figure 2: Original data set with records anonymized by adding noise to , noise to and to . Graphical representation of the distributions of the permutation distance of the match in for and for a set of random records obtained respective and independent random draws from , and .

In contrast, consider now the same data set with records, but assume that the noise added to get the anonymized data is very small. Specifically, the noise is sampled from a , the noise added to from a and the noise added to from a . Figure 3 and Table 7 show the distributions of the permutation distance for the original records (in ) and for the random records (in ).

Figure 3: Original data set with records anonymized by adding noise to , noise to and to . Graphical representation of the distributions of the permutation distance of the match in for and for a set of random records obtained respective and independent random draws from , and .
Distance Frequency Frequency
for for
0 0.0270 0.0000
1 0.2670 0.0000
2 0.2650 0.0001
3 0.2090 0.0002
4 0.1170 0.0005
5 0.0550 0.0003
6 0.0330 0.0006
7 0.0170 0.0013
8 0.0040 0.0012
9 0.0050 0.0019
10 0.0000 0.0028
11 0.0000 0.0027
12 0.0010 0.0032
13 0.0000 0.0032
14 0.0000 0.0054
0.0000 0.9766
Table 7: Original data set with records anonymized by adding noise to , noise to and to . Distributions of the permutation distance of the match in for and for a set of random records obtained respective and independent random draws from , and .

From Table 7, a match that occurs at a permutation distance of 0 must be a correct match: that is, the noise added to anonymize was so small that it did not result in re-ordering any of the attributes. Table 7 also shows that there is only a probability 0.0011 (roughly 1 over 1000) that a match is random given that its permutation distance is . In fact, comparing the distributions of the permutation distance of matches for the original data and the random data is an excellent tool for the intruder to verify on a record by record basis how accurate his record linkages are. Given the very little overlap of the distributions shown in Figure 3, the intruder can conclude that his matches are very likely to be correct ones. In this case, the anonymization procedure fails to withstand a known-plaintext attack.

The above assessment by the intruder can also be made by the data protector before releasing the data, in order to determine the optimum amount of permutation that anonymization should introduce.

The distribution of the permutation distance for the original values in is a direct function only of the level of anonymization —the higher the modification introduced by anonymization, the longer the permutation distance. The distribution of the permutation distance for random records grows with the number of records, grows with the number of attributes and, by construction, it is independent of the anonymization method and level of anonymization used (the random data set contains all possible permutations of the original records or a random large subset of them). A comprehensive discussion of the exact characteristics of the distribution of the permutation distance is beyond the scope of this paper.

To the best of our knowledge, ours is the first attempt to present a principled algorithm for the intruder to evaluate the effectiveness of the re-identification process. Prior assessment of re-identification could only be carried out by the data protector and it only focused on the percentage of misidentifications and the percentage of multiple matches (in line with the analysis made in the last paragraph of Section 5.1 above). Further developments may allow the intruder to assess the extent to which the two distributions are different (by using measures such as Hellinger’s distance) or develop formal statistical tools by treating the distribution of the match distance for the random records as the distribution of the statistic under the null hypothesis in hyphotesis testing.

Finally, note that when the anonymization method involves only permutation without noise addition (which is the case with data swapping [3] and data shuffling [19]), a data subject with access to just her own record in can not only learn the permutation distance of her record (as described in Section 4), but she can also verify whether is safe. To this end, the subject generates from the masked data ( can be used instead of , because one data set is a permutation of the other) and then checks whether a match at distance is plausible as a random match; if yes, then is safe. One may assume that the data protector has checked that the permutation distance of all records is safe, but giving each subject the possibility to check it is an attractive feature of pure-permutation anonymization.

6 Anonymization transparency towards the user

In this section, we discuss the user in the context of the permutation-paradigm of anonymization presented in Section 3. There is one tenet from data encryption that can be usefully applied to data anonymization: Kerckhoff’s principle, which states that the encryption algorithm must be entirely public, with the key being the only secret parameter. Nowadays, statistical agencies and other data releasers often refrain from publishing the parameters used in anonymization (variance of the added noise, proximity of swapped values, group size in microaggregation, etc.). The exception is when the privacy-first approach is used (based on a privacy model), in which case the anonymization parameters are explicit and dictated by the model. However, as mentioned above, most real data releases are anonymized under the utility-first approach. Withholding the parameters of anonymization is problematic for at least two reasons:

  1. The legitimate user cannot properly evaluate the utility of the anonymized data.

  2. Basing disclosure protection on the secrecy of the anonymization algorithm and its parameterization is a poor idea, as it is hard to keep that much information secret and it is better to expose algorithms and parameterizations to public scrutiny to detect any weaknesses in them.

One might argue that the parameters of an anonymization method play the role of the key in cryptography and must therefore be withheld. We contend that this is a wrong notion, because whereas cryptographic keys are randomly chosen, anonymization parameters are not (there are typical values for noise variance, etc.). The most similar thing to a cryptographic key in the context of anonymization are the random seeds used by (pseudo-)randomized anonymization methods.

It is also important to note that Kerckhoff’s principle is of no consequence to the intruder modeled according to cryptographic principles. According to our definition of the intruder, the anonymization method and the level of anonymization play no role in the re-identification process. Once the intruder has performed reverse mapping, the only remaining unknown is the random key used for permuting the values. And we have shown how the intruder can best guess the permutation used (Section 5.1) and then evaluate the accuracy of his guess without any information about the anonymization mechanism (Section 5.2). Hence, for our intruder, the claim that following Kerckhoff’s principle will result in increased disclosure risk is incorrect. Actually, following Kerckhoff’s principle harms none of the stakeholders in the microdata release (data protector, subject, intruder and user) and it is extremely valuable for the user. For these reasons, we believe that the data protector must always release details about the anonymization methods and parameters used. We formalize this notion in Definition 4.

Definition 4 (Anonymization transparency to the data user)

An anonymization method is said to be transparent to the data user when the user is given all details of the anonymization except the random seed(s) (if any are used for pseudo-randomization).

7 Conclusions and future research

We have presented a new vision of microdata anonymization that opens several new directions.

First, we have shown how knowledge of the values of the original attribute allows reverse mapping the values of the anonymized attribute into a permutation of the original attribute values. This holds for any anonymization method and for any attribute whose values can be ranked (and in fact any data are amenable to some sort of ranking). Hence, any anonymization method can be viewed as a permutation followed by a (small) noise addition. This vision applies to any anonymization method and it allows easily comparing methods in terms of the data utility and the privacy they provide.

Based on the permutation plus noise paradigm, we have stated a new privacy model, called -permuted privacy, that focuses on the minimum permutation distance achieved and on the variance of the attribute values within that distance. The advantage of this privacy model with respect to previous methods in the literature is that it is not only verifiable by the data protector, but also by each data subject having contributed a record to the original data set (subject-verifiability).

Then we have precisely defined a maximum-knowledge adversarial model in anonymization. Specifically, we have shown how our intruder can best guess the permutation achieved by an anonymization method and how he can assess the quality of his guess. The intruder’s assessment is independent of the anonymization method used and it also tells the data protector the right level of permutation needed to protect against re-identification.

Regarding the data user, we have argued why Kerckhoff’s assumption should be the rule in anonymization, just as it is the rule in encryption. Releasing the details of anonymization introduces no weakness and it is extremely useful to the user. This calls for anonymization that is transparent to the user.

We have illustrated the concepts and procedures introduced throughout the paper with a running example.

This paper opens a great number of future research lines. These include the following:

  • Extend the reverse-mapping conversion of Algorithm 1 for any type of attribute (that is, nominal in addition to numerical or ordinal).

  • Explore the consequences of the permutation paradigm of anonymization for local perturbation methods.

  • Regarding the adversarial model, rigorously characterize the distribution of the permutation distance and tackle the issues sketched at the end of Section 5.2.

  • In line with the cryptography-inspired model of anonymization, seek information-theoretic measures of anonymity focused on the mapping between the original and anonymized records output by a specific anonymization method.

  • Produce an inventory of anonymization methods in the literature that are transparent to the data user according to Definition 4. In particular, investigate to what extent deterministic methods (using no randomness seeds, e.g., microaggregation, coarsening, etc.) can be transparent.

Disclaimer and acknowledgments

The following funding sources are gratefully acknowledged: Government of Catalonia (ICREA Acadèmia Prize to the first author and grant 2014 SGR 537), Spanish Government (project TIN2011-27076-C03-01 “CO-PRIVACY”), European Commission (projects FP7 “DwB”, FP7 “Inter-Trust” and H2020 “CLARUS”), Templeton World Charity Foundation (grant TWCF0095/AB60 “CO-UTILITY”) and Google (Faculty Research Award to the first author). The first author is with the UNESCO Chair in Data Privacy. The views in this paper are the authors’ own and do not necessarily reflect the views of UNESCO, the Templeton World Charity Foundation or Google.

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