skip to main content
article

Query Evaluation under Differential Privacy

Authors Info & Claims
Published:02 November 2023Publication History
Skip Abstract Section

Abstract

Differential privacy has garnered significant attention in recent years due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of queries while maintaining differential privacy has become the holy grail in privacypreserving query release. Towards this goal, this article surveys recent advances in query evaluation under differential privacy.

References

  1. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308--318, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Abiteboul, R. Hull, and V. Vianu. Foundations of databases, volume 8. Addison-Wesley Reading, 1995.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Abo Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu, and M. Schleich. In-database learning with sparse tensors. In Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pages 325--340, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Abo Khamis, H. Q. Ngo, and A. Rudra. Faq: questions asked frequently. In Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pages 13--28, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. K. Amin, A. Kulesza, A. Munoz, and S. Vassilvtiskii. Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning, pages 263--271. PMLR, 2019.Google ScholarGoogle Scholar
  6. G. Andrew, O. Thakkar, H. B. McMahan, and S. Ramaswamy. Differentially private learning with adaptive clipping. arXiv preprint arXiv:1905.03871, 2019.Google ScholarGoogle Scholar
  7. M. Arapinis, D. Figueira, and M. Gaboardi. Sensitivity of counting queries. In International Colloquium on Automata, Languages, and Programming (ICALP), 2016.Google ScholarGoogle Scholar
  8. H. Asi and J. C. Duchi. Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems, 33, 2020.Google ScholarGoogle Scholar
  9. N. Bakibayev, T. Kocisk'y, D. Olteanu, and J. Z´avodn'y. Aggregation and ordering in factorised databases. Proceedings of the VLDB Endowment, 6(14), 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Blocki, A. Blum, A. Datta, and O. Sheffet. Differentially private data analysis of social networks via restricted sensitivity. In Proceedings of the 4th conference on Innovations in Theoretical Computer Science, pages 87--96, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Bun and T. Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635--658. Springer, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. Cai, X. Xiao, and G. Cormode. Privlava: synthesizing relational data with foreign keys under differential privacy. Proceedings of the ACM on Management of Data, 1(2):1--25, 2023.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. T.-H. H. Chan, E. Shi, and D. Song. Private and continual release of statistics. ACM Transactions on Information and System Security, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Chen and S. Zhou. Recursive mechanism: towards node differential privacy and unrestricted joins. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pages 653--664, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Dick, C. Dwork, M. Kearns, T. Liu, A. Roth, G. Vietri, and Z. S. Wu. Confidence-ranked reconstruction of census microdata from published statistics. Proceedings of the National Academy of Sciences, 120(8):e2218605120, 2023.Google ScholarGoogle ScholarCross RefCross Ref
  16. W. Dong, J. Fang, K. Yi, Y. Tao, and A. Machanavajjhala. R2T: Instance-optimal truncation for differentially privatequery evaluation with foreign keys. In Proc. ACM SIGMOD International Conference on Management of Data, 2022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. W. Dong, Q. Luo, and K. Yi. Continual observation under user-level differential privacy. In 2023 IEEE Symposium on Security and Privacy (SP), pages 2190--2207. IEEE Computer Society, 2023.Google ScholarGoogle ScholarCross RefCross Ref
  18. W. Dong, D. Sun, and K. Yi. Better than composition: How to answer multiple relational queries under differential privacy. In Proc. ACM SIGMOD International Conference on Management of Data, 2023.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. W. Dong and K. Yi. Residual sensitivity for deferentially private multi-way joins. In Proc. ACM SIGMOD International Conference on Management of Data, 2021.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. W. Dong and K. Yi. A nearly instance-optimal differentially private mechanism for conjunctive queries. In Proc. ACM Symposium on Principles of Database Systems, 2022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Dong and K. Yi. Universal private estimators. In Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pages 195--206, 2023.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing, pages 715--724, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. C. Dwork, M. Naor, O. Reingold, G. N. Rothblum, and S. Vadhan. On the complexity of differentially private data release: efficient algorithms and hardness results. In Proceedings of the forty-first annual ACM symposium on Theory of computing, pages 381--390, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. Dwork and A. Roth. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3--4):211--407, 2014.Google ScholarGoogle Scholar
  25. J. Fang, W. Dong, and K. Yi. Shifted inverse: A general mechanism for monotonic functions under user differential privacy. 2022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Z. Huang, Y. Liang, and K. Yi. Instance-optimal mean estimation under differential privacy. Advances in Neural Information Processing Systems, 2021.Google ScholarGoogle Scholar
  27. M. R. Joglekar, R. Puttagunta, and C. R´e. Ajar: Aggregations and joins over annotated relations. In Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pages 91--106, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. N. Johnson, J. P. Near, and D. Song. Towards practical differential privacy for sql queries. Proceedings of the VLDB Endowment, 11(5):526--539, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. G. Kamath, J. Li, V. Singhal, and J. Ullman. Privately learning high-dimensional distributions. In Proceedings of the 32nd Annual Conference on Learning Theory, COLT '19, pages 1853--1902, 2019.Google ScholarGoogle Scholar
  30. V. Karwa, S. Raskhodnikova, A. Smith, and G. Yaroslavtsev. Private analysis of graph structure. Proceedings of the VLDB Endowment, 4(11):1146--1157, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. P. Kasiviswanathan, K. Nissim, S. Raskhodnikova, and A. Smith. Analyzing graphs with node differential privacy. In Theory of Cryptography Conference, pages 457--476. Springer, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. M. A. Khamis, H. Q. Ngo, X. Nguyen, D. Olteanu, and M. Schleich. Learning models over relational data using sparse tensors and functional dependencies. ACM Transactions on Database Systems (TODS), 45(2):1--66, 2020.Google ScholarGoogle Scholar
  33. D. Kifer and A. Machanavajjhala. No free lunch in data privacy. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pages 193--204, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. I. Kotsogiannis, Y. Tao, X. He, M. Fanaeepour, A. Machanavajjhala, M. Hay, and G. Miklau. Privatesql: a differentially private sql query engine. Proceedings of the VLDB Endowment, 12(11):1371--1384, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. A. Kumar, M. Boehm, and J. Yang. Data management in machine learning: Challenges, techniques, and systems. In Proceedings of the 2017 ACM International Conference on Management of Data, pages 1717--1722, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. F. D. McSherry. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pages 19--30, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. A. Narayan and A. Haeberlen. Djoin: Differentially private join queries over distributed databases. In USENIX Symposium on Operating Systems Design and Implementation, pages 149--162, 2012.Google ScholarGoogle Scholar
  38. M. Nikolic, H. Zhang, A. Kara, and D. Olteanu. F-ivm: learning over fast-evolving relational data. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 2773--2776, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 75--84, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. D. Olteanu and J. Z´avodn'y. Size bounds for factorised representations of query results. ACM Transactions on Database Systems (TODS), 40(1):1--44, 2015.Google ScholarGoogle Scholar
  41. C. Palamidessi and M. Stronati. Differential privacy for relational algebra: Improving the sensitivity bounds via constraint systems. In QAPL, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  42. D. Proserpio, S. Goldberg, and F. McSherry. Calibrating data to sensitivity in private data analysis. Proceedings of the VLDB Endowment, 7(8), 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. P. Regulation. General data protection regulation. Intouch, 25:1--5, 2018.Google ScholarGoogle Scholar
  44. M. Schleich, D. Olteanu, and R. Ciucanu. Learning linear regression models over factorized joins. In Proceedings of the 2016 International Conference on Management of Data, pages 3--18, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Y. Tao, X. He, A. Machanavajjhala, and S. Roy. Computing local sensitivities of counting queries with joins. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 479--494, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. S. Vadhan. The complexity of differential privacy. In Tutorials on the Foundations of Cryptography, pages 347--450. Springer, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  47. J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao. Private release of graph statistics using ladder functions. In Proceedings of the 2015 ACM SIGMOD international conference on management of data, pages 731--745, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader