Privacy integrated data stream queries

ABSTRACT

Research on differential privacy is generally concerned with examining data sets that are static. Because the data sets do not change, every computation on them produces "one-shot" query results; the results do not change aside from randomness introduced for privacy. There are many circumstances, however, where this model does not apply, or is simply infeasible. Data streams are examples of non-static data sets where results may change as more data is streamed. Theoretical support for differential privacy with data streams has been researched in the form of differentially private streaming algorithms. In this paper, we present a practical framework for which a non-expert can perform differentially private operations on data streams. The system is built as an extension to PINQ (Privacy Integrated Queries), a differentially private programming framework for static data sets. The streaming extension provides a programmatic interface for the different types of streaming differential privacy from the literature so that the privacy trade-offs of each type of algorithm can be understood by a non-expert programmer.

References

1. T.-H. H. Chan, E. Shi, and D. Song. Private and continual release of statistics. ACM Trans. Inf. Syst. Secur., 14(3):26:1--26:24, Nov. 2011.
2. C. Dwork. Differential privacy. In ICALP, pages 1--12. Springer, 2006.
3. C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In Proc. 42nd ACM symposium on Theory of computing, STOC '10, pages 715--724, New York, NY, USA, 2010. ACM.
4. C. Dwork, M. Naor, T. Pitassi, G. N. Rothblum, and S. Yekhanin. Pan-private streaming algorithms. In Proc. ICS, 2010.
5. A. Haeberlen, B. C. Pierce, and A. Narayan. Differential privacy under fire. In Proc. 20th USENIX conference on Security, SEC'11, pages 33--33, Berkeley, CA, USA, 2011. USENIX Association.
6. G. Kellaris, S. Papadopoulos, X. Xiao, and D. Papadias. Differentially private event sequences over infinite streams. PVLDB, 7(12):1155--1166, 2014.
7. F. McSherry. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. Commun. ACM, 53(9):89--97, Sept. 2010.
8. Microsoft. Linq (language integrated query).
9. J. Reed and B. C. Pierce. Distance makes the types grow stronger: a calculus for differential privacy. In Proc. 15th ICFP, ICFP '10, pages 157--168, New York, NY, USA, 2010. ACM.
10. I. Roy, S. T. V. Setty, A. Kilzer, V. Shmatikov, and E. Witchel. Airavat: security and privacy for mapreduce. In Proc. 7th NSDI, NSDI'10, pages 20--20, Berkeley, CA, USA, 2010. USENIX Association.
11. L. Sweeney. Weaving technology and policy together to maintain confidentiality. Journal of Law, Medicine & Ethics, 25-2 & 3):98--110, 1997.

Index Terms

• Information systems

  • Information retrieval

  • Information storage systems