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Differential Privacy: A Survey of Results

  • Cynthia Dwork
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4978)

Abstract

Over the past five years a new approach to privacy-preserving data analysis has born fruit [13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related literature in the statistics, databases, theory, and cryptography communities, in that a formal and ad omnia privacy guarantee is defined, and the data analysis techniques presented are rigorously proved to satisfy the guarantee. The key privacy guarantee that has emerged is differential privacy. Roughly speaking, this ensures that (almost, and quantifiably) no risk is incurred by joining a statistical database.

In this survey, we recall the definition of differential privacy and two basic techniques for achieving it. We then show some interesting applications of these techniques, presenting algorithms for three specific tasks and three general results on differentially private learning.

Keywords

Statistical Database True Answer Statistical Query Differential Privacy Privacy Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Achugbue, J.O., Chin, F.Y.: The Effectiveness of Output Modification by Rounding for Protection of Statistical Databases. INFOR 17(3), 209–218 (1979)Google Scholar
  2. 2.
    Adam, N.R., Wortmann, J.C.: Security-Control Methods for Statistical Databases: A Comparative Study. ACM Computing Surveys 21(4), 515–556 (1989)CrossRefGoogle Scholar
  3. 3.
    Agrawal, D., Aggarwal, C.: On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems (2001)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 439–450 (2000)Google Scholar
  5. 5.
    Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release. In: Proceedings of the 26th Symposium on Principles of Database Systems, pp. 273–282 (2007)Google Scholar
  6. 6.
    Beck, L.L.: A Security Mechanism for Statistical Databases. ACM TODS 5(3), 316–338 (1980)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical Privacy: The SuLQ framework. In: Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (June 2005)Google Scholar
  8. 8.
    Blum, A., Ligett, K., Roth, A.: A Learning Theory Approach to Non-Interactive Database Privacy. In: Proceedings of the 40th ACM SIGACT Symposium on Thoery of Computing (2008)Google Scholar
  9. 9.
    Chawla, S., Dwork, C., McSherry, F., Smith, A., Wee, H.: Toward Privacy in Public Databases. In: Proceedings of the 2nd Theory of Cryptography Conference (2005)Google Scholar
  10. 10.
    Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15, 222–429 (1977)Google Scholar
  11. 11.
    Denning, D.E.: Secure Statistical Databases with Random Sample Queries. ACM Transactions on Database Systems 5(3), 291–315 (1980)zbMATHCrossRefGoogle Scholar
  12. 12.
    Denning, D., Denning, P., Schwartz, M.: The Tracker: A Threat to Statistical Database Security. ACM Transactions on Database Systems 4(1), 76–96 (1979)CrossRefGoogle Scholar
  13. 13.
    Dinur, I., Nissim, K.: Revealing Information While Preserving Privacy. In: Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 202–210 (2003)Google Scholar
  14. 14.
    Duncan, G.: Confidentiality and statistical disclosure limitation. In: Smelser, N., Baltes, P. (eds.) International Encyclopedia of the Social and Behavioral Sciences, Elsevier, New York (2001)Google Scholar
  15. 15.
    Dwork, C.: Differential Privacy. In: Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (ICALP) (2), pp. 1–12 (2006)Google Scholar
  16. 16.
    Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our Data, Ourselves: Privacy Via Distributed Noise Generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Dwork, C., McSherry, F., Talwar, K.: The Price of Privacy and the Limits of LP Decoding. In: Proceedings of the 39th ACM Symposium on Theory of Computing, pp. 85–94 (2007)Google Scholar
  18. 18.
    Dwork, C., Nissim, K.: Privacy-Preserving Datamining on Vertically Partitioned Databases. In: Franklin, M. (ed.) CRYPTO 2004. LNCS, vol. 3152, pp. 528–544. Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating Noise to Sensitivity in Private Data Analysis. In: Proceedings of the 3rd Theory of Cryptography Conference, pp. 265–284 (2006)Google Scholar
  20. 20.
    Dwork, C., Yekhanin, S.: New Efficient Attacks on Statistical Disclosure Control Mechanisms (manuscript, 2008)Google Scholar
  21. 21.
    Evfimievski, A.V., Gehrke, J., Srikant, R.: Limiting Privacy Breaches in Privacy Preserving Data Mining. In: Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 211–222 (2003)Google Scholar
  22. 22.
    Agrawal, D., Aggarwal, C.C.: On the design and Quantification of Privacy Preserving Data Mining Algorithms. In: Proceedings of the 20th Symposium on Principles of Database Systems, pp. 247–255 (2001)Google Scholar
  23. 23.
    Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)Google Scholar
  24. 24.
    Chin, F.Y., Ozsoyoglu, G.: Auditing and infrence control in statistical databases. IEEE Trans. Softw. Eng. SE-8(6), 113–139 (1982)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Dobkin, D., Jones, A., Lipton, R.: Secure Databases: Protection Against User Influence. ACM TODS 4(1), 97–106 (1979)CrossRefGoogle Scholar
  26. 26.
    Fellegi, I.: On the question of statistical confidentiality. Journal of the American Statistical Association 67, 7–18 (1972)zbMATHCrossRefGoogle Scholar
  27. 27.
    Fienberg, S.: Confidentiality and Data Protection Through Disclosure Limitation: Evolving Principles and Technical Advances. In: IAOS Conference on Statistics, Development and Human Rights(September 2000), http://www.statistik.admin.ch/about/international/fienberg_final_paper.doc
  28. 28.
    Fienberg, S., Makov, U., Steele, R.: Disclosure Limitation and Related Methods for Categorical Data. Journal of Official Statistics 14, 485–502 (1998)Google Scholar
  29. 29.
    Franconi, L., Merola, G.: Implementing Statistical Disclosure Control for Aggregated Data Released Via Remote Access. In: United Nations Statistical Commission and European Commission, joint ECE/EUROSTAT work session on statistical data confidentiality, Working Paper No.30 (April 2003), http://www.unece.org/stats/documents/2003/04/confidentiality/wp.30.e.pdf
  30. 30.
    Goldwasser, S., Micali, S.: Probabilistic Encryption. J. Comput. Syst. Sci. 28(2), 270–299 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Gusfield, D.: A Graph Theoretic Approach to Statistical Data Security. SIAM J. Comput. 17(3), 552–571 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Kasiviswanathan, S., Lee, H., Nissim, K., Raskhodnikova, S., Smith, S.: What Can We Learn Privately? (manuscript, 2007)Google Scholar
  33. 33.
    Lefons, E., Silvestri, A., Tangorra, F.: An analytic approach to statistical databases. In: 9th Int. Conf. Very Large Data Bases, October-November 1983, pp. 260–274. Morgan Kaufmann, San Francisco (1983)Google Scholar
  34. 34.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: Privacy Beyond k-Anonymity. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), p. 24 (2006)Google Scholar
  35. 35.
    McSherry, F., Talwar, K.: Mechanism Design via Differential Privacy. In: Proceedings of the 48th Annual Symposium on Foundations of Computer Science (2007)Google Scholar
  36. 36.
    Narayanan, A., Shmatikov, V.: How to Break Anonymity of the Netflix Prize Dataset, http://www.cs.utexas.edu/~shmat/shmat_netflix-prelim.pdf
  37. 37.
    Nissim, K., Raskhodnikova, S., Smith, A.: Smooth Sensitivity and Sampling in Private Data Analysis. In: Proceedings of the 39th ACM Symposium on Theory of Computing, pp. 75–84 (2007)Google Scholar
  38. 38.
    Raghunathan, T.E., Reiter, J.P., Rubin, D.B.: Multiple Imputation for Statistical Disclosure Limitation. Journal of Official Statistics 19(1), 1–16 (2003)Google Scholar
  39. 39.
    Reiss, S.: Practical Data Swapping: The First Steps. ACM Transactions on Database Systems 9(1), 20–37 (1984)zbMATHCrossRefGoogle Scholar
  40. 40.
    Rubin, D.B.: Discussion: Statistical Disclosure Limitation. Journal of Official Statistics 9(2), 461–469 (1993)Google Scholar
  41. 41.
    Shoshani, A.: Statistical databases: Characteristics, problems and some solutions. In: Proceedings of the 8th International Conference on Very Large Data Bases (VLDB 1982), pp. 208–222 (1982)Google Scholar
  42. 42.
    Samarati, P., Sweeney, L.: Protecting Privacy when Disclosing Information: k-Anonymity and its Enforcement Through Generalization and Specialization, Technical Report SRI-CSL-98-04, SRI Intl. (1998)Google Scholar
  43. 43.
    Samarati, P., Sweeney, L.: Generalizing Data to Provide Anonymity when Disclosing Information (Abstract). In: Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, p. 188 (1998)Google Scholar
  44. 44.
    Sweeney, L.: Weaving Technology and Policy Together to Maintain Confidentiality. J. Law Med Ethics 25(2-3), 98–110 (1997)CrossRefGoogle Scholar
  45. 45.
    Sweeney, L.: k-anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  46. 46.
    Sweeney, L.: Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 571–588 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  47. 47.
    Valiant, L.G.: A Theory of the Learnable. In: Proceedings of the 16th Annual ACM SIGACT Symposium on Theory of computing, pp. 436–445 (1984)Google Scholar
  48. 48.
    Xiao, X., Tao, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: SIGMOD 2007, pp. 689–700 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cynthia Dwork
    • 1
  1. 1.Microsoft Research 

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