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Cited by 156Privacy-Preserving Technologies in DataCloud Data Security SolutionsPrivacy, Security, and Data Protection

Context-Aware Generative Adversarial Privacy

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Huang, Chong, Kairouz, Peter, Chen, Xiao, Sankar, Lalitha, Rajagopal, Ram·2017·Entropy
DOI 10.3390/e19120656
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Abstract

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals’ private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP’s performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model; and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

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  • Examples of GAN-based defense models include generative adversarial privacy, privacy-preserving adversarial networks, compressive adversarial privacy, and reconstructive adversarial network.

    “Examples of defense models include generative adversarial privacy [30], privacy-preserving adversarial networks [31], compressive adversarial privacy [32], and reconstructive adversarial network [33].”

    From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [30]

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Research works printed but not linked: 84

  1. Sweeney, L. k-Anonymity: A model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 2002, 10, 557-570.
  2. Narayanan, A.; Shmatikov, V. Robust de-anonymization of large sparse datasets. In Proceedings of the IEEE Symposium on Security and Privacy (SP 2008), Oakland, CA, USA, 18-21 May 2008; pp. 111-125.
  3. Harmanci, A.; Gerstein, M. Quantification of private information leakage from phenotype-genotype data: Linking attacks. Nat. Methods 2016, 13, 251-256.
  4. Sweeney, L.; Abu, A.; Winn, J. Identifying Participants in the Personal Genome Project by Name (A Re-identification Experiment). arXiv 2013, arXiv:1304.7605. · arXiv
  5. LeFevre, K.; DeWitt, D.J.; Ramakrishnan, R. Incognito: Efficient full-domain k-anonymity. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data, Baltimore, MD, USA, 13-17 June 2005; pp. 49-60.
  6. Bayardo, R.J.; Agrawal, R. Data privacy through optimal k-anonymization. In Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), Tokyo, Japan, 5-8 April 2005; pp. 217-228.
  7. Fung, B.C.; Wang, K.; Philip, S.Y. Anonymizing classification data for privacy preservation. IEEE Trans. Knowl. Data Eng. 2007, 19, 711-725.
  8. Iyengar, V.S. Transforming data to satisfy privacy constraints. In Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada, 23-25 July 2002; pp. 279-288.
  9. Samarati, P. Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 2001, 13, 1010-1027.
  10. Wang, K.; Fung, B.C.; Philip, S.Y. Handicapping attacker's confidence: An alternative to k-anonymization. Knowl. Inf. Syst. 2007, 11, 345-368.
  11. Dwork, C. Differential privacy. In Proceedings of the 33rd International Colloquium (ICALP 2006), Venice, Italy, 10-14 July 2006.
  12. Dwork, C. Differential privacy: A survey of results. In Theory and Applications of Models of Computation: Lecture Notes in Computer Science; Springer: New York, NY, USA, 2008.
  13. Dwork, C.; Roth, A. The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci. 2014, 9, 211-407.
  14. Fienberg, S.E.; Rinaldo, A.; Yang, X. Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables. In Proceedings of the Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, 22-24 September 2010;
  15. Wang, Y.; Lee, J.; Kifer, D. Differentially private hypothesis testing, revisited. arXiv 2015, arXiv:1511.03376. · arXiv
  16. Uhlerop, C.; Slavković, A.; Fienberg, S.E. Privacy-preserving data sharing for genome-wide association studies. J. Priv. Confid. 2013, 5, 137.
  17. Yu, F.; Fienberg, S.E.; Slavković, A.B.; Uhler, C. Scalable privacy-preserving data sharing methodology for genome-wide association studies. J. Biomed. Inform. 2014, 50, 133-141.
  18. Karwa, V.; Slavković, A. Inference using noisy degrees: Differentially private β-model and synthetic graphs. Ann. Stat. 2016, 44, 87-112.
  19. Duchi, J.; Wainwright, M.J.; Jordan, M.I. Local privacy and minimax bounds: Sharp rates for probability estimation. In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, CA, USA, 5-10 December 2013; pp. 1529-1537.
  20. Duchi, J.C.; Jordan, M.I.; Wainwright, M.J. Local privacy and statistical minimax rates. In Proceedings of the 2013 IEEE 54th Annual Symposium on Foundations of Computer Science (FOCS), Berkeley, CA, USA, 26-29 October 2013; pp. 429-438.
  21. Duchi, J.; Wainwright, M.; Jordan, M. Minimax optimal procedures for locally private estimation. arXiv 2016, arXiv:1604.02390. · arXiv
  22. Kairouz, P.; Oh, S.; Viswanath, P. Extremal Mechanisms for Local Differential Privacy. J. Mach. Learn. Res. 2016, 17, 492-542.
  23. Kairouz, P.; Bonawitz, K.; Ramage, D. Discrete Distribution Estimation Under Local Privacy. In Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 19-24 June 2016; pp. 2436-2444.
  24. Ye, M.; Barg, A. Optimal schemes for discrete distribution estimation under local differential privacy. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June 2017; pp. 759-763.
  25. Raval, N.; Machanavajjhala, A.; Cox, L.P. Protecting Visual Secrets using Adversarial Nets. In Proceedings of the CVPR Workshop, Honolulu, HI, USA, 21-26 July 2017.
  26. Hayes, J.; Melis, L.; Danezis, G.; De Cristofaro, E. LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks. arXiv 2017, arXiv:cs.CR/1705.07663.
  27. Yamamoto, H. A source coding problem for sources with additional outputs to keep secret from the receiver or wiretappers. IEEE Trans. Inf. Theory 1983, 29, 918-923.
  28. Rebollo-Monedero, D.; Forne, J.; Domingo-Ferrer, J. From t-Closeness-Like Privacy to Postrandomization via Information Theory. IEEE Trans. Knowl. Data Eng. 2010, 22, 1623-1636.
  29. Varodayan, D.; Khisti, A. Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22-27 May 2011; pp. 1932-1935.
  30. Sankar, L.; Kar, S.K.; Tandon, R.; Poor, H.V. Competitive Privacy in the Smart Grid: An Information-theoretic Approach. In Proceedings of the Smart Grid Communications, Brussels, Belgium, 17-22 October 2011.
  31. Calmon, F.P.; Fawaz, N. Privacy against statistical inference. In Proceedings of the 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, 1-5 October 2012; pp. 1401-1408.
  32. Sankar, L.; Rajagopalan, S.R.; Poor, H.V. Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach. IEEE Trans. Inf. Forensics Secur. 2013, 8, 838-852.
  33. Calmon, F.P.; Varia, M.; Médard, M.; Christiansen, M.M.; Duffy, K.R.; Tessaro, S. Bounds on inference. In Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 2-4 October 2013; pp. 567-574.
  34. Sankar, L.; Rajagopalan, S.R.; Mohajer, S.; Poor, H.V. Smart Meter Privacy: A Theoretical Framework. IEEE Trans. Smart Grid 2013, 4, 837-846.
  35. Salamatian, S.; Zhang, A.; Calmon, F.P.; Bhamidipati, S.; Fawaz, N.; Kveton, B.; Oliveira, P.; Taft, N. Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy. IEEE J. Sel. Top. Signal Process. 2015, 9, 1240-1255.
  36. Liao, J.; Sankar, L.; Tan, V.F.; du Pin Calmon, F. Hypothesis testing in the high privacy regime. In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 28-30 September 2016.
  37. Calmon, F.P.; Varia, M.; Médard, M. On Information-Theoretic Metrics for Symmetric-Key Encryption and Privacy. In Proceedings of the 52nd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 1-3 October 2014.
  38. Asoodeh, S.; Alajaji, F.; Linder, T. Notes on information-theoretic privacy. In Proceedings of the 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 1-3 October 2014; pp. 1272-1278.
  39. Calmon, F.P.; Makhdoumi, A.; Médard, M. Fundamental Limits of Perfect Privacy. In Proceedings of the International Symposium on Information Theory, Hong Kong, China, 14-19 June 2015.
  40. Basciftci, Y.O.; Wang, Y.; Ishwar, P. On privacy-utility tradeoffs for constrained data release mechanisms. In Proceedings of the 2016 Information Theory and Applications Workshop (ITA), La Jolla, VA, USA, 1-5 February 2016; pp. 1-6.
  41. Kalantari, K.; Kosut, O.; Sankar, L. On the fine asymptotics of information theoretic privacy. In Proceedings of the 2016 54th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, 27-30 September 2016; pp. 532-539.
  42. Kalantari, K.; Sankar, L.; Kosut, O. On information-theoretic privacy with general distortion cost functions. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June 2017; pp. 2865-2869.
  43. Kalantari, K.; Kosut, O.; Sankar, L. Information-Theoretic Privacy with General Distortion Constraints. arXiv 2017, arXiv:1708.05468. · arXiv
  44. Asoodeh, S.; Alajaji, F.; Linder, T. On maximal correlation, mutual information and data privacy. In Proceedings of the 2015 IEEE 14th Canadian Workshop on Information Theory (CWIT), St. John's, NL, Canada, 6-9 July 2015; pp. 27-31.
  45. Asoodeh, S.; Diaz, M.; Alajaji, F.; Linder, T. Information extraction under privacy constraints. Information 2016, 7, 15.
  46. Asoodeh, S.; Alajaji, F.; Linder, T. Privacy-aware MMSE estimation. In Proceedings of the 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, 10-15 July 2016; pp. 1989-1993.
  47. Moraffah, B.; Sankar, L. Privacy-Guaranteed Two-Agent Interactions Using Information-Theoretic Mechanisms. IEEE Trans. Inf. Forensics Secur. 2017, 12, 2168-2183.
  48. Issa, I.; Kamath, S.; Wagner, A.B. An operational measure of information leakage. In Proceedings of the 2016 Annual Conference on Information Science and Systems, CISS 2016, Princeton, NJ, USA, 16-18 March 2016; pp. 234-239.
  49. Warner, S.L. Randomized response: A survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 1965, 60, 63-69.
  50. Issa, I.; Wagner, A.B. Operational definitions for some common information leakage metrics. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June 2017; pp. 769-773.
  51. Asoodeh, S.; Diaz, M.; Alajaji, F.; Linder, T. Estimation Efficiency Under Privacy Constraints. arXiv 2017, arXiv:1707.02409. · arXiv
  52. Asoodeh, S.; Diaz, M.; Alajaji, F.; Linder, T. Privacy-aware guessing efficiency. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June 2017; pp. 754-758.
  53. Kerschbaum, F. Frequency-hiding order-preserving encryption. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA, 12-16 October 2015; pp. 656-667.
  54. Dong, B.; Wang, W.; Yang, J. Secure Data Outsourcing with Adversarial Data Dependency Constraints. In Proceedings of the 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), New York, NY, USA, 9-10 April 2016; pp. 73-78.
  55. Dong, B.; Liu, R.; Wang, W.H. Prada: Privacy-preserving data-deduplication-as-a-service. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China, 3-7 November 2014; pp. 1559-1568.
  56. Alemi, A.; Fischer, I.; Dillon, J.; Murphy, K. Deep Variational Information Bottleneck. In Proceedings of the International Conference on Learning Representations, Toulon, France, 24-26 April 2017.
  57. Giraldo, L.G.S.; Principe, J.C. Rate-Distortion Auto-Encoders. arXiv 2013, arXiv:1312.7381. · arXiv
  58. Zhang, Y.; Ozay, M.; Sun, Z.; Okatani, T. Information Potential Auto-Encoders. arXiv 2017, arXiv:1706.04635. · arXiv
  59. Theis, L.; Shi, W.; Cunningham, A.; Huszár, F. Lossy Image Compression with Compressive Autoencoders. In Proceedings of the International Conference on Learning Representations, Toulon, France, 24-26 April 2017.
  60. Moon, K.R.; Sricharan, K.; Hero, A.O. Ensemble estimation of mutual information. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25-30 June 2017; pp. 3030-3034.
  61. Mirza, M.; Osindero, S. Conditional generative adversarial nets. arXiv 2014, arXiv:1411.1784. · arXiv
  62. Schmidhuber, J.H. Learning factorial codes by predictability minimization. Neural Comput. 1992, 4, 863-879.
  63. Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 8-13 December 2014; pp. 2672-2680.
  64. Morris, J. On single-sample robust detection of known signals with additive unknown-mean amplitude-bounded random interference. IEEE Trans. Inf. Theory 1980, 26, 199-209.
  65. Shamai, S.; Verdú, S. Worst-case power-constrained noise for binary-input channels. IEEE Trans. Inf. Theory 1992, 38, 1494-1511.
  66. Morris, J. On single-sample robust detection of known signals with additive unknown-mean amplitude-bounded random interference-II: The randomized decision rule solution (Corresp.). IEEE Trans. Inf. Theory 1981, 27, 132-136.
  67. Morris, J.M.; Dennis, N.E. A random-threshold decision rule for known signals with additive amplitude-bounded nonstationary random interference. IEEE Trans. Commun. 1990, 38, 160-164.
  68. Root, W.L. Communications through unspecified additive noise. Inf. Control 1961, 4, 15-29.
  69. Tang, J.; Korolova, A.; Bai, X.; Wang, X.; Wang, X. Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12. arXiv 2017, arXiv:1709.02753. · arXiv
  70. Verdú, S. α-mutual information. In Proceedings of the 2015 Information Theory and Applications Workshop (ITA), San Diego, CA, USA, 1-6 February 2015.
  71. Sugiyama, M.; Borgwardt, K.M. Measuring statistical dependence via the mutual information dimension. DIM 2013, 10, 1.
  72. Hamm, J. Minimax Filter: Learning to Preserve Privacy from Inference Attacks. arXiv 2016, arXiv:1610.03577. · arXiv
  73. Liu, C.; Chakraborty, S.; Mittal, P. DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications. arXiv 2017, arXiv:1702.06159. · arXiv
  74. Xu, K.; Cao, T.; Shah, S.; Maung, C.; Schweitzer, H. Cleaning the Null Space: A Privacy Mechanism for Predictors. In Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 4-9 February 2017.
  75. Abadi, M.; Andersen, D.G. Learning to protect communications with adversarial neural cryptography. arXiv 2016, arXiv:1610.06918. · arXiv
  76. Edwards, H.; Storkey, A. Censoring representations with an adversary. arXiv 2015, arXiv:1511.05897. · arXiv
  77. Hinton, G.E. Deep belief networks. Scholarpedia 2009, 4, 5947.
  78. Nguyen, T.; Sanner, S. Algorithms for direct 0-1 loss optimization in binary classification. In Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA, 16-21 June 2013; pp. 1085-1093.
  79. Zhang, G.P. Neural networks for classification: A survey. IEEE Trans. Syst. Man Cybern. Part C 2000, 30, 451-462.
  80. Tang, Y. Deep learning using linear support vector machines. arXiv 2013, arXiv:1306.0239. · arXiv
  81. Lillo, W.E.; Loh, M.H.; Hui, S.; Zak, S.H. On solving constrained optimization problems with neural networks: A penalty method approach. IEEE Trans. Neural Netw. 1993, 4, 931-940.
  82. Eckstein, J.; Yao, W. Augmented Lagrangian and alternating direction methods for convex optimization: A tutorial and some illustrative computational results. RUTCOR Res. Rep. 2012, 32, 1-34.
  83. Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.; Devin, M.; et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv 2016, arXiv:1603.04467. · arXiv
  84. Van Trees, H.L. Detection, Estimation, and Modulation Theory; John Wiley & Sons: Hoboken, NJ, USA, 2004.

Cited by

Sorted by citations

Authors
Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R.
Affiliations
  • Arizona State University (US)
  • Stanford University (US)
ORCID
Venue
Entropy
Publisher
MDPI AG
Published
2017-12-01
Volume
19
Issue
12
Pages
656
ISSN
1099-4300 (ISSN-L)
DOI
10.3390/e19120656
OpenAlex
W2765886485
Cited by
156
References
5
Citations by year
2026: 2 · 2025: 10 · 2024: 16 · 2023: 13 · 2022: 23 · 2021: 21 · 2020: 30 · 2019: 25 · 2018: 15 · 2017: 1
FWCI
11.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Entropy (publishedVersion, cc-by)
  • arXiv (Cornell University) (publishedVersion)
  • Arizona State University Library Digital Repository (Arizona State University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (CAREER Award CCF-1350914, CAREER Award ECCS-1554178, CPS-1545043)
  • Department of Energy SunShot Office Solar Program (31003)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
context-aware privacy, privacy-utility trade-off, generative adversarial networks, minimax game, inference attacks, differential privacy, information-theoretic privacy

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