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Your search keyword '"Xiao, Xiaokui"' showing total 32 results
Start Over Author "Xiao, Xiaokui" Journal proceedings of the vldb endowment
32 results on '"Xiao, Xiaokui"'

2. Optimal random perturbation at multiple privacy levels

Author

Xiao, Xiaokui, Tao, Yufei, and Chen, Minghua

Abstract

Random perturbation is a popular method of computing anonymized data for privacy preserving data mining. It is simple to apply, ensures strong privacy protection, and permits effective mining of a large variety of data patterns. However, all the existing studies with good privacy guarantees focus on perturbation at a single privacy level. Namely, a fixeddegree of privacy protection is imposed on allanonymized data released by the data holder. This drawback seriously limits the applicability of random perturbation in scenarios where the holder has numerous recipients to which different privacy levels apply.Motivated by this, we study the problem of multi-level perturbation, whose objective is to release multiple versions of a dataset anonymized at different privacy levels. The challenge is that various recipients may collude by sharing their data to infer privacy beyond their permitted levels. Our solution overcomes this obstacle, and achieves two crucial properties. First, collusion is useless, meaning that the colluding recipients cannot learn anything more than what the most trustable recipient (among the colluding recipients) already knows alone. Second, the data each recipient receives can be regarded (and hence, analyzed in the same way) as the output of conventional uniform perturbation. Besides its solid theoretical foundation, the proposed technique is both space economical and computationally efficient. It requires O (n+m)expected space, and produces a new anonymized version in O(n+ log m) expected time, where nis the cardinality of the original dataset, and mthe number of versions released previously. Both bounds are optimal under the realistic assumption that n» m.

Published
2024
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3. Output perturbation with query relaxation

Author

Xiao, Xiaokui and Tao, Yufei

Abstract

Given a dataset containing sensitive personal information, a statistical database answers aggregate queries in a manner that preserves individual privacy. We consider the problem of constructing a statistical database using output perturbation, which protects privacy by injecting a small noise into each query result. We show that the state-of-the-art approach, e-differential privacy, suffers from two severe deficiencies: it (i) incurs prohibitive computation overhead, and (ii) can answer only a limited number of queries, after which the statistical database has to be shut down. To remedy the problem, we develop a new technique that enforces e-different privacy with economical cost. Our technique also incorporates a query relaxationmechanism, which removes the restriction on the number of permissible queries. The effectiveness and efficiency of our solution are verified through experiments with real data.

Published
2024
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4. Skellam mixture mechanism

Author

Bao, Ergute, Zhu, Yizheng, Xiao, Xiaokui, Yang, Yin, Ooi, Beng Chin, Tan, Benjamin Hong Meng, and Aung, Khin Mi Mi

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, General Engineering, Cryptography and Security (cs.CR), Machine Learning (cs.LG)
Abstract

Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with differential privacy ( DP ), which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through federated learning , using both secure multiparty computation ( MPC ) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject real-valued noise , are fundamentally incompatible with MPC, which exchanges finite-field integers among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility. Motivated by this, we propose Skellam mixture mechanism (SMM), a novel approach to enforcing DP on models built via federated learning. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of DP deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.

Published
2022

7. CGM

Author

Bao, Ergute, primary, Yang, Yin, additional, Xiao, Xiaokui, additional, and Ding, Bolin, additional

Published
2021
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18. PTrider

Author

Chen, Lu, primary, Gao, Yunjun, additional, Liu, Zixian, additional, Xiao, Xiaokui, additional, Jensen, Christian S., additional, and Zhu, Yifan, additional

Published
2018
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20. Probesim

Author

Liu, Yu, primary, Zheng, Bolong, additional, He, Xiaodong, additional, Wei, Zhewei, additional, Xiao, Xiaokui, additional, Zheng, Kai, additional, and Lu, Jiaheng, additional

Published
2017
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22. HubPPR

Author

Wang, Sibo, primary, Tang, Youze, additional, Xiao, Xiaokui, additional, Yang, Yin, additional, and Li, Zengxiang, additional

Published
2016
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26. Functional mechanism

Author

Zhang, Jun, primary, Zhang, Zhenjie, additional, Xiao, Xiaokui, additional, Yang, Yin, additional, and Winslett, Marianne, additional

Published
2012
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28. Low-rank mechanism

Author

Yuan, Ganzhao, primary, Zhang, Zhenjie, additional, Winslett, Marianne, additional, Xiao, Xiaokui, additional, Yang, Yin, additional, and Hao, Zhifeng, additional

Published
2012
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