Abstract
Personalized local differential privacy is a privacy protection mechanism that aims to safeguard the privacy of data by using personalized approaches, while also providing practical data analysis results. It offers more flexible and precise privacy protection capabilities compared to traditional local differential privacy. By employing distinct privacy protection strategies for different users, it can better meet users’ privacy requirements while minimizing the impact on data. However, existing mechanisms for personalized local differential privacy suffer from issues such as low query accuracy and poor data utility. These issues need to be addressed to improve the effectiveness and practicality of personalized local differential privacy.
In this work, we have proposed a framework of personalized differential privacy in the shuffle model. This framework introduces individualized perturbation to the data locally and then reshuffles the records in the dataset, disrupting the original order of the data and breaking the correlations between data points. This approach aims to achieve a higher level of privacy protection. We have validated the practicality and superiority of this framework on four different types of real-world datasets.
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References
Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SIAM J. Comput. 40(3), 793–826 (2011)
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 429–438. IEEE (2013)
Erlingsson, Ú., Pihur, V., Korolova, A.: Rappor: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054–1067 (2014)
Ding, B., Kulkarni, J., Yekhanin, S.: Collecting telemetry data privately. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, pp. 127–135 (2015)
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., Thakurta, A.: Amplification by shuffling: From local to central differential privacy via anonymity. In: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2468–2479. SIAM (2019)
Balle, B., Bell, J., Gascón, A., Nissim, K.: The privacy blanket of the shuffle model. In: Boldyreva, A., Micciancio, D. (eds.) CRYPTO 2019. LNCS, vol. 11693, pp. 638–667. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26951-7_22
Cheu, A., Smith, A., Ullman, J., Zeber, D., Zhilyaev, M.: Distributed differential privacy via shuffling. In: Ishai, Y., Rijmen, V. (eds.) EUROCRYPT 2019. LNCS, vol. 11476, pp. 375–403. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17653-2_13
Balcer. V. Cheu, A.: Separating local & shuffled differential privacy via histograms," arXiv preprint arXiv:1911.06879 (2019)
Beimel, A., Haitner, I., Nissim, K., Stemmer, U.: On the round complexity of the shuffle model. In: Pass, R., Pietrzak, K. (eds.) TCC 2020. LNCS, vol. 12551, pp. 683–712. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64378-2_24
Chen, L., Ghazi, B., Kumar, R., Manurangsi, P.: On distributed differential privacy and counting distinct elements, arXiv preprint arXiv:2009.09604 (2020)
Jorgensen, Z., Yu, T., Cormode, G.: Conservative or liberal? personalized differential privacy. In: 2015 IEEE 31St International Conference on Data Engineering, pp. 1023–1034. IEEE (2015)
Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 289–300. IEEE (2016)
Yiwen, N., Yang, W., Huang, L., Xie, X., Zhao, Z., Wang, S.: A utility-optimized framework for personalized private histogram estimation. IEEE Trans. Knowl. Data Eng. 31(4), 655–669 (2018)
Shen, X., Jiang, H., Chen, Y., Wang, B., Gao, L.: Pldp-fl: federated learning with personalized local differential privacy. Entropy 25(3), 485 (2023)
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Bittau, A., et al.: Prochlo: strong privacy for analytics in the crowd. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 441–459 (2017)
Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–69 (1965)
Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Kairouz, P., Bonawitz, K., Ramage, D.: Discrete distribution estimation under local privacy. In: International Conference on Machine Learning, pp. 2436–2444. PMLR (2016)
Wang, S.: Privacy amplification via shuffling: Unified, simplified, and tightened, arXiv preprint arXiv:2304.05007 (2023)
Becker, B., Kohavi, R.: Adult, UCI Machine Learning Repository (1996). https://doi.org/10.24432/C5XW20
Moro, S., Cortez, P., Rita, P.: A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 62, 22–31 (2014)
Warwick, S. T. T. S. C. A. N., Ford, W.: Abalone, UCI Machine Learning Repository, (1995). https://doi.org/10.24432/C55C7W
Bohanec, M.: Car Evaluation, UCI Machine Learning Repository (1997). https://doi.org/10.24432/C5JP48
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Yang, R. et al. (2024). Personalized Differential Privacy in the Shuffle Model. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_33
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DOI: https://doi.org/10.1007/978-981-99-9785-5_33
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