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We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks paradigm.
S. Oya, C. Troncoso, and F. Pérez-González, “Back to the drawing board: Revisiting the design of optimal location privacy-preserving mechanisms,” in Proc. of CCS . ACM, 2017, pp. 1959–1972
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Y. Zhu and R. Bettati, “Anonymity vs. information leakage in anonymity systems,” in Proc. of ICDCS . IEEE, 2005, pp. 514–524
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T. M. Cover and J. A. Thomas, Elements of Information Theory , 2nd ed. J. Wiley & Sons, Inc., 2006
2006
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N. Santhi and A. Vardy, “On an improvement over Rényi’s equivocation bound,” 2006, presented at the 44-th Annual Allerton Conf. on Communication, Control, and Computing, September 2006. Available at http://arxiv.org/abs/cs/0608087
2006
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C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Proc. of TCC , ser. LNCS, vol. 3876. Springer, 2006, pp. 265–284
2006
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K. Chatzikokolakis, C. Palamidessi, and P. Panangaden, “On the Bayes risk in information-hiding protocols,” J. of Comp. Security , vol. 16, no. 5, pp. 531–571, 2008
2008
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G. Smith, “On the foundations of quantitative information flow,” in Proc. of FOSSACS , ser. LNCS, vol. 5504. Springer, 2009, pp. 288–302
2009
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A. McIver, L. Meinicke, and C. Morgan, “Compositional Closure for Bayes Risk in Probabilistic Noninterference,” in Proc. of ICALP , ser. LNCS, vol. 6199. Springer, 2010, pp. 223–235
2010
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X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth Int. Conf. on AI and Statistics , ser. Proceedings of Machine Learning Research, vol. 9. PMLR, 2010, pp. 249–256
2010
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M. S. Alvim, M. E. Andrés, K. Chatzikokolakis, and C. Palamidessi, “On the relation between Differential Privacy and Quantitative Information Flow,” in Proc. of ICALP , ser. LNCS, vol. 6756. Springer, 2011, pp. 60–76
2011
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A. De, “Lower bounds in differential privacy,” in Proc. of TCC . Springer, 2012, pp. 321–338
2012
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M. S. Alvim, K. Chatzikokolakis, C. Palamidessi, and G. Smith, “Measuring information leakage using generalized gain functions,” in Proc. of CSF , 2012, pp. 265–279
2012
Earlier work this paper cites.
M. E. Andrés, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geo-indistinguishability: differential privacy for location-based systems,” in Proc. of CCS . ACM, 2013, pp. 901–914
2013
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Local privacy and statistical minimax rates,” in Proc. of FOCS . IEEE Computer Society, 2013, pp. 429–438
2013
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K. Chatzikokolakis, M. E. Andrés, N. E. Bordenabe, and C. Palamidessi, “Broadening the scope of Differential Privacy using metrics,” in Proc. of PETS , ser. LNCS, vol. 7981. Springer, 2013, pp. 82–102
2013
Earlier work this paper cites.
N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Optimal geo-indistinguishable mechanisms for location privacy,” in Proc. of CCS , 2014
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 . Curran Associates, Inc., 2014, pp. 2672–2680
2014
Cited alongside, same era.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proc. of CCS , ser. CCS ’15. ACM, 2015, pp. 1322–1333
2015
Cited alongside, same era.
R. Shokri, “Privacy games: Optimal user-centric data obfuscation,” Proceedings on Privacy Enhancing Technologies , vol. 2015, no. 2, pp. 299–315, 2015
2015
Cited alongside, same era.
G. Cherubin, “Bayes, not naïve: Security bounds on website fingerprinting defenses,” PoPETs , vol. 2017, no. 4, pp. 215–231, 2017
2017
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2017
Later among the works it cites.
C. Huang, P. Kairouz, X. Chen, L. Sankar, and R. Rajagopal, “Context-aware generative adversarial privacy,” Entropy , vol. 19, no. 12, 2017
2017
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J. Hamm, “Minimax filter: Learning to preserve privacy from inference attacks,” J. Mach. Learn. Res. , vol. 18, no. 1, pp. 4704–4734, 2017
2017
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I. Mironov, “Rényi differential privacy,” in Proc. of CSF , 2017, pp. 263–275
2017
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2016
Cited alongside, same era.
M. S. Alvim, K. Chatzikokolakis, A. McIver, C. Morgan, C. Palamidessi, and G. Smith, “Axioms for information leakage,” in Proc. of CSF , 2016, pp. 77–92
2016
Cited alongside, same era.
P. Cuff and L. Yu, “Differential privacy as a mutual information constraint,” in Proc. of CCS , ser. CCS ’16. ACM, 2016, pp. 43–54
2016
Cited alongside, same era.
H. Edwards and A. J. Storkey, “Censoring representations with an adversary,” in Proc. of ICLR , 2016
2016
Cited alongside, same era.
S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Proc. of NIPS , 2016, pp. 271–279
2016
Cited alongside, same era.
Y. O. Basciftci, Y. Wang, and P. Ishwar, “On privacy-utility tradeoffs for constrained data release mechanisms,” in Proc. of ITA . IEEE, 2016, pp. 1–6
2016
Cited alongside, same era.
2016
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in Proc. of S&P . IEEE Computer Society, 2017, pp. 3–18
2017
Cited alongside, same era.
A. Pyrgelis, C. Troncoso, and E. D. Cristofaro, “Knock knock, who’s there? membership inference on aggregate location data,” in Proc. of NDSS . The Internet Society, 2018
2018
Later among the works it cites.
J. Jia and N. Z. Gong, “Attriguard: A practical defense against attribute inference attacks via adversarial machine learning,” in 27th USENIX Security Symposium (USENIX Security 18) . USENIX Association, 2018, pp. 513–529
2018
Later among the works it cites.
J. Hayes and O. Ohrimenko, “Contamination attacks and mitigation in multi-party machine learning,” in Proc. of NIPS . Curran Associates Inc., 2018, pp. 6604–6616
2018
Later among the works it cites.
Z. Ren, Y. J. Lee, and M. S. Ryoo, “Learning to anonymize faces for privacy preserving action detection,” in Proc. of ECCV , ser. LNCS, vol. 11205. Springer, 2018, pp. 639–655
2018
Later among the works it cites.
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm, “Mutual information neural estimation,” in Proceedings of the 35th Int. Conf. on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 2018, pp. 531–540
2018
Later among the works it cites.
J. Hayes, L. Melis, G. Danezis, and E. D. Cristofaro, “LOGAN: membership inference attacks against generative models,” PoPETs , vol. 2019, no. 1, pp. 133–152, 2019
2019
Closest in time.
L. Melis, C. Song, E. D. Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in Proc. of S&P . IEEE, 2019, pp. 497–512
2019
Closest in time.
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, R. Lee, S. P. Bhavnani, J. B. Byrd, and C. S. Greene, “Privacy-preserving generative deep neural networks support clinical data sharing,” Circulation: Cardiovascular Quality and Outcomes , vol. 12, no. 7, p. e005122, 2019
2019
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P. K. Rubenstein, O. Bousquet, J. Djolonga, C. Riquelme, and I. O. Tolstikhin, “Practical and consistent estimation of f-divergences,” in Proc. of NIPS , 2019, pp. 4072–4082
2019
Closest in time.