Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access
Author:
Affiliation:
1. IT University of Copenhagen, Copenhagen, Denmark
2. Basic Algorithms Research Copenhagen & IT University of Copenhagen, Copenhagen, Denmark
3. Basic Algorithms Research Copenhagen & University of Copenhagen, Copenhagen, Denmark
Funder
Villum Fonden
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3460120.3484735
Reference15 articles.
1. Differential Privacy on Finite Computers;Balcer Victor;J. Priv. Confidentiality,2019
2. Simultaneous Private Learning of Multiple Concepts;Bun Mark;J. Mach. Learn. Res.,2019
3. Universal classes of hash functions
4. Differentially private summaries for sparse data
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Efficient and Secure Quantile Aggregation of Private Data Streams;IEEE Transactions on Information Forensics and Security;2023
2. Locally Differentially Private Sparse Vector Aggregation;2022 IEEE Symposium on Security and Privacy (SP);2022-05
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