Abstract
In the differential privacy interactive framework, data sets need to be used to answer multiple queries. With the gradual consumption of the privacy budget, the risk of privacy disclosure increases. Therefore, it is essential to save and track the consumption of the privacy budget, which should not exceed the limit given by the privacy budget. Therefore, firstly, this paper optimizes the Gaussian mechanism to reduce the query response time; Then a Continuous Query Differential Privacy Mechanism (CQDPM) is designed to save the overhead of privacy budget and improve the availability of data; Use the blockchain to record the privacy budget to facilitate the query of the usage of the privacy budget; Finally, a data integrity verification algorithm is proposed by using blockchain. Experiments show that the proposed mechanism reduces the query response time, effectively saves the privacy budget overhead under the same privacy budget limit, and has higher data availability.
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Balle, B., Wang, Y.X.: Improving the gaussian mechanism for differential privacy: analytical calibration and optimal denoising. In: International Conference on Machine Learning, pp. 394–403. PMLR (2018)
Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the internet of things. IEEE Access 4, 2292–2303 (2016)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
Dwork, C., Lei, J.: Differential privacy and robust statistics. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, pp. 371–380 (2009)
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Dwork, C., Rothblum, G.N.: Concentrated differential privacy. arXiv preprint arXiv:1603.01887 (2016)
Hao, C., Peng, C., Zhang, P.: Selection method of differential privacy protection parameter under repeated attack. Comput. Eng. 44(7), 145–149 (2018)
Hardt, M., Rothblum, G.N.: A multiplicative weights mechanism for privacy-preserving data analysis. In: 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pp. 61–70. IEEE (2010)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decentralized Bus. Rev. 21260 (2008)
Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, pp. 75–84 (2007)
Roth, A., Roughgarden, T.: Interactive privacy via the median mechanism. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing, pp. 765–774 (2010)
Sommer, D.M., Meiser, S., Mohammadi, E.: Privacy loss classes: the central limit theorem in differential privacy. Proc. Priv. Enhancing Technol. 2019(2), 245–269 (2019)
Wang, D., Long, S.: Boosting the accuracy of differentially private in weighted social networks. Multimedia Tools Appl. 78(24), 34801–34817 (2019). https://doi.org/10.1007/s11042-019-08092-0
Yang, M., Margheri, A., Hu, R., Sassone, V.: Differentially private data sharing in a cloud federation with blockchain. IEEE Cloud Comput. 5(6), 69–79 (2018)
Zhao, J., et al.: Reviewing and improving the gaussian mechanism for differential privacy. arXiv preprint arXiv:1911.12060 (2019)
Zhao, Y., et al.: A blockchain-based approach for saving and tracking differential-privacy cost. IEEE Internet Things J. 8(11), 8865–8882 (2021)
Zhu, T., Li, G., Zhou, W., Yu, P.S.: Differential Privacy and Applications. AIS, vol. 69. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62004-6
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Thanks to the National Natural Science Foundation of China (NO. 62062020)(NO. 62002081)(NO. 62002080)
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Ouyang, H., Lyu, H., Long, S., Liu, H., Ding, H. (2022). A Blockchain-Based Continuous Query Differential Privacy Algorithm. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_57
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DOI: https://doi.org/10.1007/978-3-030-96772-7_57
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