Results 221 to 230 of about 27,791 (249)

On robustness and local differential privacy

Annals of Statistics, 2023
It is of soaring demand to develop statistical analysis tools that are robust against contamination as well as preserving individual data owners' privacy. In spite of the fact that both topics host a rich body of literature, to the best of our knowledge, we are the first to systematically study the connections between the optimality under Huber's ...
Li, Mengchu, Berrett, Thomas B., Yu, Yi
exaly   +3 more sources

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Privacy preserving classification on local differential privacy in data centers

Journal of Parallel and Distributed Computing, 2020
With 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, Zhijie Han
exaly   +5 more sources

Differential Privacy in the Local Setting

Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics, 2018
Differential 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.
openaire   +1 more source

Quantum Differential Privacy in the Local Model

IEEE Transactions on Information Theory
Differential privacy provides a robust framework for protecting sensitive data, while maintaining its utility for computation. In essence, a differentially private algorithm takes as input the data of multiple parties, and returns an output disclosing minimal information about any individual party.
Armando Angrisani, Elham Kashefi
openaire   +1 more source

Local Differential Privacy for Data Streams

2020
The 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
openaire   +1 more source

Randomized requantization with local differential privacy

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
In 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, Anand D. Sarwate, Narayan B. Mandayam   +2 more
openaire   +1 more source

Towards Measuring Fairness for Local Differential Privacy

2023
Local 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, Vicenç Torra, David Megías 0001   +2 more
openaire   +2 more sources

Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

IEEE Transactions on Knowledge and Data Engineering, 2018
Recommender 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, Sungwook Kim 0001, Junbum Shin, Xiaokui Xiao   +3 more
openaire   +3 more sources

Local Differential Privacy for Data Clustering

Proceedings of the 21st International Conference on Security and Cryptography
This study presents an innovative framework that utilizes Local Differential Privacy (LDP) to address the challenge of data privacy in practical applications of data clustering. Our framework is designed to prioritize the protection of individual data privacy by empowering users to proactively safeguard their information before it is shared to any ...
Bruder, Lisa, Alishahi, Mina
openaire   +1 more source