Differential privacy: On the trade-off between utility and information leakage

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7140 LNCS, Issue , 2012, Pages 39-54

  Alvim, Mário S ^a   Andrés, Miguel E ^a   Chatzikokolakis, Konstantinos ^a   Degano, Pierpaolo ^b   Palamidessi, Catuscia ^a  

^a ÉCOLE POLYTECHNIQUE   (France)

^b UNIVERSITY OF PISA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BAYES RISK; DATA SETS; DATABASE COMMUNITY; DIFFERENTIAL PRIVACIES; INFORMATION FLOWS; INFORMATION LEAKAGE; MIN-ENTROPY; PRIVACY PROTECTION; QUERY MECHANISMS; QUERY SYSTEMS; SECRET INFORMATION;

ENTROPY; INFORMATION THEORY; QUERY PROCESSING;

SEARCH ENGINES;

EID: 84863951613     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29420-4_3     Document Type: Conference Paper

References (27)

1. Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15, 429-444 (1977)
2. Dwork, C.: Differential Privacy. In: Bugliesi, M., Preneel, B., Sassone, V.,Wegener, I. (eds.) ICALP 2006, Part II. LNCS, vol. 4052, pp. 1-12. Springer, Heidelberg (2006)
3. Dwork, C.: Differential privacy in new settings. In: Proc. of the Twenty-First Annual ACMSIAM Symposium on Discrete Algorithms, SODA 2010, Austin, Texas, USA, January 17-19, pp. 174-183. SIAM (2010)
4. Dwork, C.: A firm foundation for private data analysis. Communications of the ACM 54(1), 86-96 (2011)
5. Dwork, C., Lei, J.: Differential privacy and robust statistics. In: Proc. of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, May 31-June 2, pp. 371-380. ACM (2009)
6. Clark, D., Hunt, S.,Malacaria, P.: Quantitative analysis of the leakage of confidential data. In: Proc. of QAPL. Electr. Notes Theor. Comput. Sci., vol. 59(3), pp. 238-251. Elsevier (2001)
7. Clark, D., Hunt, S., Malacaria, P.: Quantitative information flow, relations and polymorphic types. J. of Logic and Computation 18(2), 181-199 (2005)
8. Clarkson, M.R., Myers, A.C., Schneider, F.B.: Belief in information flow. J. of Comp. Security 17(5), 655-701 (2009)
9. Köpf, B., Basin, D.A.: An information-theoretic model for adaptive side-channel attacks. In: Proc. of CCS, pp. 286-296. ACM (2007)
10. Malacaria, P.: Assessing security threats of looping constructs. In: Proc. of POPL, pp. 225-235. ACM (2007)
11. Malacaria, P., Chen, H.: Lagrange multipliers and maximum information leakage in different observational models. In: Proc. of PLAS, pp. 135-146. ACM (2008)
12. Smith, G.: On the Foundations of Quantitative Information Flow. In: de Alfaro, L. (ed.) FOSSACS 2009. LNCS, vol. 5504, pp. 288-302. Springer, Heidelberg (2009)
13. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379-423, 625-656 (1948)
14. Rényi, A.: On Measures of Entropy and Information. In: Proc. of the 4th Berkeley Symposium on Mathematics, Statistics, and Probability, pp. 547-561 (1961)
15. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. J. Wiley & Sons, Inc. (2006)
16. Braun, C., Chatzikokolakis, K., Palamidessi, C.: Quantitative notions of leakage for one-try attacks. In: Proc. of MFPS. ENTCS, vol. 249, pp. 75-91. Elsevier (2009)
17. Braun, C., Chatzikokolakis, K., Palamidessi, C.: Compositional Methods for Information-Hiding. In: Amadio, R.M. (ed.) FOSSACS 2008. LNCS, vol. 4962, pp. 443-457. Springer, Heidelberg (2008)
18. Chatzikokolakis, K., Palamidessi, C., Panangaden, P.: On the Bayes risk in information-hiding protocols. J. of Comp. Security 16(5), 531-571 (2008)
19. Kasiviswanathan, S.P., Smith, A.: A note on differential privacy: Defining resistance to arbitrary side information. CoRR abs/0803.3946 (2008)
20. Ghosh, A., Roughgarden, T., Sundararajan, M.: Universally utility-maximizing privacy mechanisms. In: Proc. of the 41st Annual ACMSymposium on Theory of Computing, STOC 2009, pp. 351-360. ACM (2009)
21. Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. SIAM J. Comput. 38(1), 97-139 (2008)
22. Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Johnson, D.S., Feige, U. (eds.) STOC, pp. 75-84. ACM (2007)
23. Bernardo, J.M., Smith, A.F.M.: Bayesian Theory. J. Wiley & Sons, Inc. (1994)
24. Alvim, M.S., Chatzikokolakis, K., Degano, P., Palamidessi, C.: Differential privacy versus quantitative information flow. Technical report (2010)
25. Barthe, G., Köpf, B.: Information-theoretic bounds for differentially private mechanisms. In: Proc. of CSF (to appear, 2011)
26. Clarkson, M.R., Schneider, F.B.: Quantification of integrity Tech. Rep. (2011), http://hdl.handle.net/1813/22012
27. Heusser, J., Malacaria, P.: Applied Quantitative Information Flow and Statistical Databases. In: Degano, P., Guttman, J.D. (eds.) FAST 2009. LNCS, vol. 5983, pp. 96-110. Springer, Heidelberg (2010)