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1 Measures of Disclosure Risk and Harm Measures of Disclosure Risk and Harm Diane Lambert, Journal of Official Statistics, 9 (1993), pp. 313-331 Jim Lynch.

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Presentation on theme: "1 Measures of Disclosure Risk and Harm Measures of Disclosure Risk and Harm Diane Lambert, Journal of Official Statistics, 9 (1993), pp. 313-331 Jim Lynch."— Presentation transcript:

1 1 Measures of Disclosure Risk and Harm Measures of Disclosure Risk and Harm Diane Lambert, Journal of Official Statistics, 9 (1993), pp Jim Lynch NISS/SAMSI & University of South Carolina

2 2 Measures of Disclosure Risk and Harm Introduction-Discussion (Section 7) What is Disclosure? Risk of Perceived Identification Modeling the Intruder Risk of True Identification Disclosure Harm

3 3 Discussion (Section 7) It is the intruder, and not the structure of the data alone, that controls disclosure. When the intruder is sure enough that a released record belongs to a respondent –There is a re­identification. –It may be incorrect, but the intruder perceives there to be a re­identification.

4 4 Discussion (Section 7) The risk of perceived disclosure and the risk of true disclosure cannot be measured without considering the seriousness of the threat posed by the intruder's strategy. The harm that follows from a re­identification –Depends on the attributes, if any, that the intruder infers about the target –The harm cannot be measured without considering the strategy that the intruder uses to infer sensitive attributes. Once the intruder's strategy is modeled, disclosure risk and harm can be evaluated Risk is measured in terms of probabilities Harm is measured in losses or costs.

5 5 Discussion (Section 7) All the agency can do to reduce disclosure risk or harm is –to mask the data before release –or carefully select the individuals and organizations that are given the data, or both. The models developed here imply that masking and releasing only a subset of records does not necessarily protect against disclosure. Masking may lower the risk of true re­identification –But it may also lead to false re­identifications and false inferences about attributes. –The fact that inferred attributes may be wrong may be little comfort to the respondent whose record is re­ identified.

6 6 Discussion (Section 7) Masking also complicates data analysis –An agency cannot be expected to predict and minimize all the effects of masking on all the analyses of interest. –Nor is it reasonable to expect the data analyst to describe how the data will be analyzed before the data are obtained so that the agency can verify that the conclusions will be the same for the masked data as they would have been for the original data. –Future masking techniques may preserve more general features of the data, but for now data masked enough to preserve confidentiality can be a challenge to analyze appropriately.

7 7 Discussion (Section 7) It does seem reasonable to put some of the burden for protecting confidentiality on the researcher. –Institutions and researchers have to abide by all sorts of conditions in experiments involving humans. –The experience in those and other areas ought to provide some guidance on protecting respondents in agency databases from unscrupulous intruders. –Would not necessarily remove the need for some masking, but it might reduce the need for heroic masking that severely limits the usefulness of the data. “Confidentiality issues for medical data miners,” Jules J. Berman, Pathology Informatics Cancer Diagnosis Program, DCTD, NCI, NIH,

8 8 Discussion (Section 7) One could argue that models of disclosure are hopeless because the issues are too complex and the intruder too mysterious. This paper, though, argues that models of disclosure are indispensable. –They force definitions and assumptions to be stated explicitly. –When the assumptions are realistic, models of disclosure can lead to practical measures of disclosure risk and harm.

9 9 What is Disclosure? Key Attributes –Useful for identification but usually not sensitive –E.g., age, location, marital status, and profession Sensitive Attributes –Disease, debts, credit rating Scenario: A sample of records is released –Obvious identifiers removed –Some attributes left intact such as marital status –Others modified to protect confidentiality Incomes truncated, professions grouped more coarsely, and ages on pairs of records swapped, some attributes on some records might be missing or imputed.

10 10 What is Disclosure? Two types of identity disclosure –Identification or Re-identification Equivalent to inadvertent release of an identifiable record –Attribute Disclosure Occurs when the intruder believes something new has been learned about the respondent. May occur with or without re-identification E.g., the intruder may narrow the list of possible target records to two with nearly the same value of a sensitive attribute. Then the attribute is disclosed although the target record is not located. Or two records may be averaged so the released record belongs to no one. Yet the debt on the averaged record may disclose something about the debt carried by the targeted individual. The agency must decide whether attribute disclosures without identifications are important.

11 11 What is Disclosure? Considers only disclosures that involve re­ identifications but NOT attribute disclosures without re­identifications. Attribute disclosures that result from re­ identification are considered to the extent that they harm the respondent. In this paper, the risk of disclosure is the risk of re­identifying a released record and the harm from disclosure depends on what is learned from the identification.

12 12 What is Disclosure? Attribute disclosures that do not involve identification are ignored This assumes that all intruders first look for the record that is most likely to be correct and then take information about the targeted attribute from that record. Intruders with other strategies are ignored.

13 13 What is Disclosure? Includes –true and false re­identifications and –true and false attribute disclosures. –Correct and incorrect inferences can be distinguished if desired (as happens with measures of harm) It distinguishes between –true identification and true attribute disclosure and –perceived identification and perceived attribute disclosure (the intruder believes the information is correct) where, in the former, when correct inferences are to be prevented and in the latter when perceived inferences are to be prevented.

14 14 The Risk of Perceived Identification Basic Premise: Disclosure is limited only to the extent that the intruder is discouraged from making any inferences, correct or incorrect, about a particular target respondent.

15 15 The Risk of Perceived Identification Format (Similar to Jerry’s last time) –Population of N records denoted Z –A random sample of n masked records X=(x 1,…, x n ) with k attributes –Masking suppresses attributes in Z, adds random noise, truncates outliers, or swaps values of an attribute between records. Knowing this, which, if any, record in the released file should be linked to the target respondent’s record Y?

16 16 The Risk of Perceived Identification Rational Intruder has two options. –1. Decide that one of the released records belongs to the target respondent. (i.e., link the i th released record x i to the target record Y. –2. Decide not to link (the null link) any released record to Y, perhaps because none of the released records is close enough to what the intruder expects for Y or perhaps because too many released records are close to what the intruder expects for Y. The decision not Rational intruder chooses the link (non­null or null) believed most likely to be correct whenever any incorrect choice incurs the same positive loss and a correct link incurs no loss. (See Duncan and Lambert (1989) for details.)

17 17 The Risk of Perceived Identification

18 18 The Risk of Perceived Identification Other Measures

19 19 Modeling the Intruder Example 4.1 – Pop of 2 Records: N=2=n One continuous attribute Intruder makes judgments about the M(Y), the masked version of target Y Series of judgments leads to intruder modeling prior about M(Y) as lognormal  with  (0,1) (prior denoted f 1 (x)) Information about the other respondent, Y’, is modeled as M(Y’)~lognormal(2,1) denoted f 2 (x) E(M(Y))=1.65 and E(M(Y’))=12.2 Released data is X=(7,20)

20 20 Modeling the Intruder Example 4.1 – A “Posterior Calculation ” p 1 =P(M(Y)=7|X=(7,20))=P(M(Y’)=20|X=(7,20)) =f 1 (7)f 2 (20)/[f 1 (7)f 2 (20)+f 2 (7)f 1 (20)]=.89 In the original population Y=Z 1

21 21 Modeling the Intruder Example 4.1 Suppose only one record is released and it is x=7. Then, p 1 =P(M(Y) is selected and M(Y)=7|X=(7,20)) =.5f 1 (7)/[.5f 1 (7))+.5f 2 (7)]=.13 In this case, D(X)=max(.13,.87)=.87

22 22 Modeling the Intruder Example 4.2 – n of N records Intruder believes that the i th record in pop Z will be appear as M i =M(Y i )~ f i (x) The probability that the n th released record belongs to target Y 1 is p n =P(Y 1 is sampled and M 1 =x n |X) =P(x n is sampled from f 1 and x 1,…, x n-1 are sampled from f 2,…, f N )/P(x 1,…, x n are sampled from f 1,…, f N )

23 23 Modeling the Intruder Example n=2 of N=3 records Non-unique Records

24 24 Example n=1 of N=2 records Unknown respondents may be re­identifiable Intruder’s priors on Z –Y 1 ~Unif[-4,4], Y 2 ~N(0,1), x 1 =-2.25

25 25 Example n=10 of N=100 records Sampling by itself need not protect confidentiality Target is thought to be the smallest in Pop The Priors: Y 1 ~LogN(0,.5), Y 2,…,Y 100 LogN(2,.5) Masking is iid multiplicative LogN(0,.5) Uncertainty in the released records (masking+intruder prior) M 1 ~LogN(0,1)=f 1, M 2,…,M 100 LogN(2,1)=f 2 X=(.05,.14, 1.5, 2.4, 3.2, 3.8, 4.6, 8.7, 10.3, 10.7)

26 26 Example n=10 of N=100 records Sampling by itself need not protect confidentiality

27 27 Example n=10 of N=100 records Sampling by itself need not protect confidentiality Values of P j1 (X)

28 28 Risk of True Identification The agency cannot control the intruder's perceptions and actions once the data are released. All it can do is count the number of true identifications for an intruder with a given set of beliefs about the target and source file. A reasonable measure of the risk of true identification, then, is simply the fraction of released records (or number of released records) that an intruder can correctly re­identify.

29 29 Risk of True Identification Distinguishes “Risk of Matching” (Spruill, ) with “Risk of True Identification” (Risk of Matching is the proportion of masked records whose closest source records are the actual source records generated them) To illustrate Risk of True identification, consider the following example where N is large and n small so that we can calculate using sampling with replacement

30 30 Risk of True Identification

31 31 Risk of True Identification

32 32 Risk of True Identification Risk of True Identification is low (zero if.078 is too low to link). Look down columns 15 and 30. Risk of matching is not zero for both records? Look across rows. 32 matched with 30 which is incorrect but 35 is matched with 30? (Why not 40.7?) Claimed risk of matching is ½? Risk of perceived re-identification? Look down all columns. If 1- the sum of the column is more than the max of the column the intruder is wasting their time. This is an assumption about the intruder that their rational decision is that the record for that column has not been released. In this example, this is true for all the columns.

33 33 Disclosure Harm Just Considers Harm to Respondent (not to agencies, researchers, etc) whose released record has been re- identified or perceived to have been reidentified Scenario –Masked Data is released X=(x 1,…,x n ) where = x i =(x i1,…,x ik ) and x i is a binary attribute of interest. Assume that the target record is Y 1 and that the intruder has linked Y 1 to x 1.Let x -11 =(x 12,…,x 1k ) and X -1 =(x -11,x 2 …,x n ) –Because of masking the intruder believes, independent of everything else, that x 11 = Y 11 with probability q x 11 = 1-Y 11 with probability 1- q

34 34 Disclosure Harm

35 35 Disclosure Harm-Logistic Regression

36 36 Disclosure Harm-Measures of Harm Harm H(Y 11,X) is a variable that takes on various values depending on the action that the intruder takes based on their their strategy These values are losses and are –0 if record is not identified –c FN if re-identification is incorrect and Y 11 is not inferred –c TN if re-identification is correct and Y 11 is not inferred –and

37 37 Disclosure Harm Some Possibly Delusionary Closing Comments Think of the source data, Y, as the parameter The released data, X, is the sample This is somewhat like a two person game where the agency plays the role of Mother Nature and the intruder is the other person The agency controls the way it generates the released data

38 38 Disclosure Harm Some Possibly Delusionary Closing Comments When we describe the mechanism/structure/model that is used to generate released data we are specifying somewhat the model X|Y. –Are we totally specifying this? –There are at the very least some nuisance parameters regarding weights, e.g. Is there a meaningful interpretation in randomizing over the parameter from the agencies perspective? Perhaps we should reverse the roles of the agency and the intruder. The parameter is then the intruder’s strategy. In any event Lambert is suggesting that we need to model the intruder strategy and formulate the problem from a decision theory standpoint.

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