K-匿名性
参考资料

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↑  L. Sweeney. k-anonymity: a model for protecting privacy （页面存档备份，存于）. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 卌, 2002; 557-570.
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↑  Roberto J. Bayardo; Rakesh Agrawal.  (PDF). 2005: 217–28  [2021-08-11]. ISBN 978-0-7695-2285-2. ISSN 1084-4627. S2CID 17044848. doi:10.1109/ICDE.2005.42. （原始内容存档 (PDF)于2020-11-23）. Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a nontrivial dataset under a general model of the problem.  |journal=被忽略 (帮助)
↑  Adam Meyerson; Ryan Williams.  (PDF). New York, NY: ACM. 2004: 223–8  [2021-08-11]. ISBN 978-1581138580. S2CID 6798963. doi:10.1145/1055558.1055591. （原始内容存档 (PDF)于2014-05-28）. The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to delete from the relation. We also present a polynomial time algorithm for optimal k-anonymity that achieves an approximation ratio independent of the size of the database, when k is constant. In particular, it is a O(k log k)-approximation where the constant in the big-O is no more than 4. However, the runtime of the algorithm is exponential in k. A slightly more clever algorithm removes this condition, but is a O(k logm)-approximation, where m is the degree of the relation. We believe this algorithm could potentially be quite fast in practice.  |journal=被忽略 (帮助)
↑  Kenig, Batya; Tassa, Tamir. . Data Mining and Knowledge Discovery. 2012, 25: 134–168. S2CID 14158546. doi:10.1007/s10618-011-0235-9.
↑  Machanavajjhala, Ashwin; Kifer, Daniel; Gehrke, Johannes; Venkitasubramaniam, Muthuramakrishnan. . ACM Transactions on Knowledge Discovery from Data. March 2007, 1 (1): 3–es. ISSN 1556-4681. S2CID 679934. doi:10.1145/1217299.1217302. Background Knowledge Attack. Alice has a pen-friend named Umeko who is admitted to the same hospital as Bob and whose patient records also appear in the table shown in Figure 2. Alice knows that Umeko is a 21-year-old Japanese female who currently lives in zip code 13068. Based on this information, Alice learns that Umeko’s information is contained in record number 1,2,3, or 4. Without additional information, Alice is not sure whether Umeko caught a virus or has heart disease. However, it is well known that Japanese have an extremely low incidence of heart disease. Therefore Alice concludes with near certainty that Umeko has a viral infection.
↑  Aggarwal, Charu C. . . Trondheim, Norway. 2005. ISBN 1-59593-154-6.
↑  de Montjoye, Yves-Alexandre; César A. Hidalgo; Michel Verleysen; Vincent D. Blondel. . Scientific Reports. 2013-03-25, 3: 1376  [2021-08-11]. Bibcode:2013NatSR...3E1376D. PMC 3607247 . PMID 23524645. doi:10.1038/srep01376.
↑  Angiuli, Olivia; Joe Blitzstein; Jim Waldo. . ACM Queue. ACM.   [2021-08-11]. （原始内容存档于2021-04-22）.
↑  Angiuli, Olivia; Jim Waldo. . IEEE Computer Society Intl Conference on Computers, Software, and Applications. June 2016: 589–593. ISBN 978-1-4673-8845-0. S2CID 17716908. doi:10.1109/COMPSAC.2016.198.
↑  Li, Lucy; Pal, Bijeeta; Ali, Junade; Sullivan, Nick; Chatterjee, Rahul; Ristenpart, Thomas. . 2019-09-04. arXiv:1905.13737  [cs.CR].
1 2  Ali, Junade; Dyo, Vladimir. . 17th International Conference on Security and Cryptography (SECRYPT 2020). 2020: 572–579. ISBN 978-989-758-446-6. S2CID 218629946. arXiv:2005.06580 . doi:10.5220/0009825105720579.
↑  Thomas, Kurt; Pullman, Jennifer; Yeo, Kevin; Raghunathan, Ananth; Kelley, Patrick Gage; Invernizzi, Luca; Benko, Borbala; Pietraszek, Tadek; Patel, Sarvar; Boneh, Dan; Bursztein, Elie. . 2019: 1556–1571  [2020-05-22]. ISBN 9781939133069. （原始内容存档于2021-04-15） （英语）.
↑  Demir, Levent; Kumar, Amrit; Cunche, Mathieu; Lauradoux, Cédric. . Communications Surveys and Tutorials, IEEE Communications Society. 2018, 20 (1): 551  [2020-05-22]. S2CID 3571244. doi:10.1109/COMST.2017.2747598. （原始内容存档于2020-11-27） （英语）.