ABSTRACT
Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data.
This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.
- D. Aldous and J. Fill. Reversible markov chains and random walks on graphs, 2002.Google Scholar
- R. Bassily, A. Groce, J. Katz, and A. Smith. Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy. In FOCS, 2013. Google ScholarDigital Library
- K. Chaudhuri, D. Hsu, and S. Song. The large margin mechanism for differentially private maximization. In NIPS, 2014. Google ScholarDigital Library
- K. Chaudhuri, C. Monteleoni, and A. Sarwate. Differentially private empirical risk minimization. JMLR, 12:1069--1109, 2011. Google ScholarDigital Library
- R. Chen, N. Mohammed, B. C. Fung, B. C. Desai, and L. Xiong. Publishing set-valued data via differential privacy. VLDB Endowment, 2011.Google ScholarDigital Library
- T. M. Cover and J. A. Thomas. Elements of information theory. John Wiley & Sons, 2012.Google ScholarDigital Library
- C. Dwork and J. Lei. Differential privacy and robust statistics. In STOC, 2009. Google ScholarDigital Library
- C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography, 2006. Google ScholarDigital Library
- C. Dwork and A. Roth. The algorithmic foundations of differential privacy. TCS, 9(3--4):211--407, 2013. Google ScholarDigital Library
- K. Ellis et al. Multi-sensor physical activity recognition in free-living. In UbiComp '14 Adjunct. Google ScholarDigital Library
- K. Ellis et al. Physical activity recognition in free-living from body-worn sensors. In SenseCam '13. Google ScholarDigital Library
- K. Ellis et al. Hip and wrist accelerometer algorithms for free-living behavior classification. Medicine and science in sports and exercise, 48(5):933--940, 2016.Google Scholar
- L. Fan, L. Xiong, and V. Sunderam. Differentially private multi-dimensional time series release for traffic monitoring. In DBSec, 2013.Google ScholarCross Ref
- A. Ghosh and R. Kleinberg. Inferential privacy guarantees for differentially private mechanisms. arXiv preprint arXiv:1603.01508, 2016.Google Scholar
- M. Hardt and A. Roth. Beyond worst-case analysis in private singular vector computation. In STOC, 2013. Google ScholarDigital Library
- M. Hay, V. Rastogi, G. Miklau, and D. Suciu. Boosting the accuracy of differentially private histograms through consistency. VLDB, 2010. Google ScholarDigital Library
- X. He, G. Cormode, A. Machanavajjhala, C. M. Procopiuc, and D. Srivastava. Dpt: differentially private trajectory synthesis using hierarchical reference systems. Proc. of VLDB, 2015. Google ScholarDigital Library
- X. He, A. Machanavajjhala, and B. Ding. Blowfish privacy: tuning privacy-utility trade-offs using policies. In SIGMOD '14, pages 1447--1458, 2014. Google ScholarDigital Library
- S. Kessler, E. Buchmann, and K. Böhm. Deploying and evaluating pufferfish privacy for smart meter data. Karlsruhe Reports in Informatics, 1, 2015.Google Scholar
- D. Kifer and A. Machanavajjhala. Pufferfish: A framework for mathematical privacy definitions. ACM Trans. Database Syst., 39(1):3, 2014. Google ScholarDigital Library
- D. Koller and N. Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009. Google ScholarDigital Library
- C. Li, M. Hay, V. Rastogi, G. Miklau, and A. McGregor. Optimizing linear counting queries under differential privacy. In PODS '10. Google ScholarDigital Library
- C. Li and G. Miklau. An adaptive mechanism for accurate query answering under differential privacy. VLDB, 2012. Google ScholarDigital Library
- C. Liu, S. Chakraborty, and P. Mittal. Dependence makes you vulnerable: Differential privacy under dependent tuples. In NDSS 2016, 2016.Google ScholarCross Ref
- A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber. Privacy: Theory meets practice on the map. In ICDE, 2008. Google ScholarDigital Library
- S. Makonin, B. Ellert, I. V. Bajic, and F. Popowich. Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Scientific Data, 3(160037):1--12, 2016.Google Scholar
- F. McSherry and K. Talwar. Mechanism design via differential privacy. In FOCS, 2007. Google ScholarDigital Library
- K. Nissim, S. Raskhodnikova, and A. Smith. Smooth sensitivity and sampling in private data analysis. In STOC, 2007. Google ScholarDigital Library
- V. Rastogi and S. Nath. Differentially private aggregation of distributed time-series with transformation and encryption. In SIGMOD, 2010. Google ScholarDigital Library
- A. Sarwate and K. Chaudhuri. Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data. Signal Processing Magazine, IEEE, 30(5):86--94, Sept 2013.Google ScholarCross Ref
- S. Song, K. Chaudhuri, and A. Sarwate. Stochastic gradient descent with differentially private updates. In GlobalSIP Conference, 2013.Google ScholarCross Ref
- B. Stoddard, Y. Chen, and A. Machanavajjhala. Differentially private algorithms for empirical machine learning. arXiv preprint arXiv:1411.5428, 2014.Google Scholar
- Y. Xiao and L. Xiong. Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC CCS. Google ScholarDigital Library
- Y. Xiao, L. Xiong, and C. Yuan. Differentially private data release through multidimensional partitioning. In Workshop on Secure Data Management. Google ScholarDigital Library
- B. Yang, I. Sato, and H. Nakagawa. Bayesian differential privacy on correlated data. In SIGMOD '15. Google ScholarDigital Library
Index Terms
- Pufferfish Privacy Mechanisms for Correlated Data
Recommendations
Bayesian Differential Privacy on Correlated Data
SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of DataDifferential privacy provides a rigorous standard for evaluating the privacy of perturbation algorithms. It has widely been regarded that differential privacy is a universal definition that deals with both independent and correlated data and a ...
Pufferfish: A framework for mathematical privacy definitions
In this article, we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application. The goal of Pufferfish is to allow ...
Attribute Privacy: Framework and Mechanisms
FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and TransparencyEnsuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been devoted to methods for protecting individual privacy during analyses of ...
Comments