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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.
Salas, Julián +2 more
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Collecting Preference Rankings Under Local Differential Privacy
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019In this paper, we initiate the study of collecting preference rankings under local differential privacy. The key technical challenge comes from the fact that the number of possible rankings increases factorially in the number of items to rank. In practical settings, this number could be large, leading to excessive injected noise. To solve this problem,
Xiang Cheng +5 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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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 +4 more
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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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Privacy-Preserving Genomic Statistical Analysis Under Local Differential Privacy
2023Akito Yamamoto, Tetsuo Shibuya
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Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma
Ca-A Cancer Journal for Clinicians, 2020Aaron J Grossberg +2 more
exaly
Cancer statistics in China, 2015
Ca-A Cancer Journal for Clinicians, 2016Rongshou Zheng +2 more
exaly
Local Cancer Recurrence: The Realities, Challenges, and Opportunities for New Therapies
Ca-A Cancer Journal for Clinicians, 2018David A Mahvi +2 more
exaly