Defamation Caused by Anonymization
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The document discusses the implications of k-anonymization in relation to defamation, particularly how it can harm individuals, such as job candidates, by linking them to negative reputations based on their group associations. It outlines a case study illustrating how a candidate's chances of employment can be affected if he is anonymized in a group containing individuals with undesirable backgrounds. The proposed solution is to improve k-anonymization algorithms to minimize the presence of undesirable individuals in groups while balancing the accuracy of the anonymized data.
Defamation Caused by Anonymization
- 1. Defamation Caused by Anonymization Hiroshi Nakagawa (The University of Tokyo)
- 2. name age sex address Location at 2016/6/6 12:00 John 35 M Bunkyo hongo 11 K consumer finance shop Dan 30 M Bunkyo Yusima 22 T University Jack 33 M Bunkyo Yayoi 33 T University Bill 39 M Bunkyo Nezu 44 Y hospital name age sex address Location at 2016/6/6 12:00 John 30’s M Bunkyo K consumer finance shop Dan 30’s M Bunkyo T University Jack 30’s M Bunkyo T University Bill 30’s M Bunkyo Y hospital ４-anonymize Dan , Jack and Bill are not recognized a person different from John by 4-anonyumity, all four persons are suspected to stay at K consumer finance shopk-anonymization provokes defamation on Dan, Jack and Bill.
- 3. k-anonymity provokes defamation in sub area aggregation k-anonymmized area : at least k people are in this area consumer finance shop This university student who is trying to find a job, is suspected to stay at consumer finance shop, and this situation is not good for his job seeking process. Defama tion
- 4. Why defamation happens? • Case study – A job candidate who is a good university student. – He is in k people group that includes at least one person who went to a consumer finance shop. – A company he tries to take entrance examination does not want hire a person who goes to a consumer finance shop. – He is suspected to go to a consumer finance shop. defamation!
- 5. Back ground situation of defamation • Case study cont. – If the company deletes him from candidates, it must use another time and money, say X, to check another candidate: – If the company hires a bad buy, it will suffer a certain amount of damage, say Y, by his bad behavior. – Then if the expected value of Y is more than X, the company becomes very negative, otherwise not negative about him. – This is a defamation model from an economical point of view.
- 6. Back ground situation of defamation • Case study cont. – Another factor is the probability that he actually went to a consumer finance shop. – This probability is proportional to the number of consumer finance shop visitors, say s, in k people of k-anonymity group = s/k. – Y is proportional to s/k – Then the relation is sketched in the figure on next slide.
- 7. 1 0 1 The subjective probability of the company suspects him The expected damage if the company hires him The money the company has to spend for checking another candidate s/k In this area, the company does not pay if it suspects him In this area, the company should suspect him to avoid the expected damage C The border line between defamation or not
- 8. Solution • Then the solution is simple: – Make the border line as small as possible. – But how? • We can revise k-anonymization algorithm in order to minimize the number of bad behavior guys in k- anonymity group. – This revision, however, reduce the accuracy of the data. – Then the problem comes to be a optimization problem: Maximize Accuracy of data subject to number of bad guys ≤ 1 in k-anonymity group
- 9. Outline of proposed algorithm 1. Do k-anonymization. 2. If one group includes more than one bad guys ① Then combine this and two nearest groups ② Do k-anonymization to this combined group to make two groups that includes at most one bad guys. ③ If step ② fails, ④ then go back to one step in 1. Do k-anonymization, namely try to find another generalization in k- anonymization.
- 10. A consumer finance shop is devided into 4 parts to reduce # of poepole visit it is less or equal than one K-anonymity area isdevided into 4 areas A concumer finance shop
- 11. name age sex address Location at 2016/6/6 12:00 John 35 M Bunkyo hongo 11 K consumer finance shop Dan 30 M Bunkyo Yusima 22 K consumer finance shop Jack 33 M Bunkyo Yayoi 33 K consumer finance shop Bill 39 M Bunkyo Nezu 44 K consumer finance shop Exchange one person to make DB 2-diversity By 2-diversifying, Ales becomes strongly suspected to be at K consumer finance shop l-diversity provokes defamation l-diversity makes situation worse These values shows all four is at K consumer finance shop name age sex address Location at 2016/6/6 12:00 John 30’s M Bunkyo K consumer finance shop Dan 30’s M Bunkyo K consumer finance shop Jack 30’s M Bunkyo K consumer finance shop Alex 30’s M Bunkyo Ｔ Univeristy
- 12. Thank you for your attention