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Privacy Preserving Classification on Local Differential Privacy in Data Centers
Journal of Parallel and Distributed Computing, 2020With the rise of cloud service providers and the continuous virtualization of data centers, data center networks are also developing rapidly. As data centers become more and more complex, the demand for security increases dramatically. This paper discusses the privacy inherent in data centers.
Peng Li, Jing He, Mengjiao Guo
exaly +5 more sources
Local Differential Privacy for Data Streams
2020The dynamic change, huge data size, and complex structure of the data stream have made it very difficult to be analyzed and protected in real-time. Traditional privacy protection models such as differential privacy which need to rely on the trusted servers or companies, and this will increase the uncertainty of protecting streaming privacy.
Xianjin Fang, Qingkui Zeng, Gaoming Yang
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Differential Privacy in the Local Setting
Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics, 2018Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. While many algorithms have been developed for data publishing and analysis satisfying differential privacy, there have been few deployment of such techniques.
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Towards Measuring Fairness for Local Differential Privacy
2023Local differential privacy (LDP) approaches provide data subjects with the strong privacy guarantees of Differential Privacy under the scenario of untrusted data curators. They are used by companies (e.g., Google’s RAPPOR) to collect potentially sensitive data from clients through randomized response.
Julián Salas +2 more
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Randomized requantization with local differential privacy
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016In this paper we study how individual sensors can compress their observations in a privacy-preserving manner. We propose a randomized requantization scheme that guarantees local differential privacy, a strong model for privacy in which individual data holders must mask their information before sending it to an untrusted third party.
Sijie Xiong +2 more
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Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2018Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can ...
Hyejin Shin +3 more
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Multiple Privacy Regimes Mechanism for Local Differential Privacy
2019Local differential privacy (LDP), as a state-of-the-art privacy notion, enables users to share protected data safely while the private real data never leaves user’s device. The privacy regime is one of the critical parameters balancing between the correctness of the statistical result and the level of user’s privacy.
Yutong Ye 0002 +4 more
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A Novel Local Differential Privacy Federated Learning Under Multi-Privacy Regimes
Expert Systems With Applications, 2023Shuping Dang, Jinchuan Tang, Gaojie Chen
exaly
PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy
IEEE Internet of Things Journal, 2023Baocang Wang, Yange Chen, Zhen Zhao
exaly
Local Differential Privacy for Data Collection and Analysis
Neurocomputing, 2021Xinyu Yang, Kwok-Yan Lam, Xuebin Ren
exaly