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Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition (2405.04344v2)
Published 7 May 2024 in cs.AI and cs.CR
Abstract: Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represented in general metric space, such as text data encoded as word embeddings or geo-location data on the road network or grid maps. To derive an optimal data perturbation mechanism under mDP, a widely used method is linear programming (LP), which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in large-scale mDP. In this paper, our objective is to develop a new computation framework to enhance the scalability of the LP-based mDP. Considering the connections established by the mDP constraints among the secret records, we partition the original secret dataset into various subsets. Building upon the partition, we reformulate the LP problem for mDP and solve it via Benders Decomposition, which is composed of two stages: (1) a master program to manage the perturbation calculation across subsets and (2) a set of subproblems, each managing the perturbation derivation within a subset. Our experimental results on multiple datasets, including geo-location data in the road network/grid maps, text data, and synthetic data, underscore our proposed mechanism's superior scalability and efficiency.
- 2020. openstreetmap. https://www.openstreetmap.org/. Accessed: 2020-04-07.
- 2024. linprog: Solve linear programming problems. https://www.mathworks.com/help/optim/ug/linprog.html. Accessed in January 2024.
- 2024. word2vec: Map word to embedding vector. https://www.mathworks.com/help/textanalytics/ref/wordembedding.word2vec.html. Accessed in January 2024.
- Geo-indistinguishability: Differential Privacy for Location-based Systems. In Proc. of ACM CCS. 901–914.
- Optimal Geo-Indistinguishable Mechanisms for Location Privacy. In Proc. of ACM CCS. 251–262.
- Broadening the Scope of Differential Privacy Using Metrics. In Proc. of Privacy Enhancing Technologies, Emiliano De Cristofaro and Matthew Wright (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 82–102.
- Constructing elastic distinguishability metrics for location privacy. Privacy Enhancing Technologies (PoPETs) 2015 (2015), 156–170. http://www.degruyter.com/view/j/popets.2015.2015.issue-2/popets-2015-0023/popets-2015-0023.xml
- Solving Linear Programs in the Current Matrix Multiplication Time. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (Phoenix, AZ, USA) (STOC 2019). Association for Computing Machinery, New York, NY, USA, 938–942. https://doi.org/10.1145/3313276.3316303
- Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography. Springer Berlin Heidelberg, 265–284.
- K. Fawaz and K. G. Shin. 2014. Location Privacy Protection for Smartphone Users. In Proc. of ACM CCS (Scottsdale, Arizona, USA). ACM, New York, NY, USA, 239–250. https://doi.org/10.1145/2660267.2660270
- Generalised Differential Privacy for Text Document Processing. In Proc. of Principles of Security and Trust, Flemming Nielson and David Sands (Eds.). Springer International Publishing, Cham, 123–148.
- Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, Los Alamitos, CA, USA, 210–219. https://doi.org/10.1109/ICDM.2019.00031
- Oluwaseyi Feyisetan and Shiva Kasiviswanathan. 2021. Private Release of Text Embedding Vectors. In Proc. of the First Workshop on Trustworthy Natural Language Processing. Association for Computational Linguistics, Online, 15–27. https://doi.org/10.18653/v1/2021.trustnlp-1.3
- Christodoulos A. Floudas. 2009. Generalized benders decompositionGeneralized Benders Decomposition. Springer US, Boston, MA, 1162–1175. https://doi.org/10.1007/978-0-387-74759-0_201
- L. Hagen and A.B. Kahng. 1992a. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11, 9 (1992), 1074–1085. https://doi.org/10.1109/43.159993
- L. Hagen and A.B. Kahng. 1992b. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 11, 9 (1992), 1074–1085. https://doi.org/10.1109/43.159993
- Frederick S. Hillier. 2008. Linear and Nonlinear Programming. Stanford University.
- Balancing utility and scalability in metric differential privacy. In Proc. of UAI 2022. https://www.amazon.science/publications/balancing-utility-and-scalability-in-metric-differential-privacy
- H. Lütkepohl. 1996. Handbook of Matrices. Springer. https://books.google.com/books?id=sGbVPgAACAAJ
- Frank McSherry and Kunal Talwar. 2007. Mechanism Design via Differential Privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS’07). 94–103. https://doi.org/10.1109/FOCS.2007.66
- On Spectral Clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Vol. 14. MIT Press. https://proceedings.neurips.cc/paper_files/paper/2001/file/801272ee79cfde7fa5960571fee36b9b-Paper.pdf
- D. P. Palomar and Mung Chiang. 2006. A tutorial on decomposition methods for network utility maximization. IEEE Journal on Selected Areas in Communications 24, 8 (Aug 2006), 1439–1451. https://doi.org/10.1109/JSAC.2006.879350
- User Customizable and Robust Geo-Indistinguishability for Location Privacy. In Proc. of International Conference on Extending Database Technology (EDBT).
- C. Qiu and A. C. Squicciarini. 2019. Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing Via Geo-Indistinguishability. In Proc. of IEEE ICDCS. 1061–1071.
- Time-Efficient Geo-Obfuscation to Protect Worker Location Privacy over Road Networks in Spatial Crowdsourcing. In Proc. of ACM CIKM.
- Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability. IEEE Transactions on Mobile Computing (2022), 1–1. https://doi.org/10.1109/TMC.2020.3037911
- TrafficAdaptor: An Adaptive Obfuscation Strategy for Vehicle Location Privacy Against Vehicle Traffic Flow Aware Attacks. In Proc. of ACM SIGSPATIAL.
- The Benders decomposition algorithm: A literature review. European Journal of Operational Research 259, 3 (2017), 801–817. https://doi.org/10.1016/j.ejor.2016.12.005
- Protecting Location Privacy: Optimal Strategy Against Localization Attacks. In Proc. of ACM CCS. 617–627.
- Daniel W. Stroock. 2010. Probability Theory: An Analytic View (2nd ed.). Cambridge University Press.
- Ulrike von Luxburg. 2007. A Tutorial on Spectral Clustering. arXiv:0711.0189Â [cs.DS]
- Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation. In Proc. of ACM WWW. 627–636.
- Differential Location Privacy for Sparse Mobile Crowdsensing. In 2016 IEEE 16th International Conference on Data Mining (ICDM). 1257–1262. https://doi.org/10.1109/ICDM.2016.0169
- Robin J Wilson. 1986. Introduction to Graph Theory. John Wiley & Sons, Inc., USA.
- Dynamic Differential Location Privacy with Personalized Error Bounds. In Proc. of IEEE NDSS.
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