63 citations · 279 across the 28 of their papers we have counts for
5 papers · 1 filter
Estimating Numerical Distributions under Local Differential Privacy
Zitao Li, Tianhao Wang, Milan Lopuhaä-Zwakenberg +2
When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before se…
Improving Frequency Estimation under Local Differential Privacy
Milan Lopuhaä-Zwakenberg, Zitao Li, Boris Škorić +1
Local Differential Privacy protocols are stochastic protocols used in data aggregation when individual users do not trust the data aggregator with their private data. In such proto…
Information-theoretic metrics for Local Differential Privacy protocols
Milan Lopuhaä-Zwakenberg, Boris Škorić, Ninghui Li
Local Differential Privacy (LDP) protocols allow an aggregator to obtain population statistics about sensitive data of a userbase, while protecting the privacy of the individual us…
Improving Utility and Security of the Shuffler-based Differential Privacy
Tianhao Wang, Bolin Ding, Min Xu +5
When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the centr…
Locally Differentially Private Frequency Estimation with Consistency
Tianhao Wang, Milan Lopuhaä-Zwakenberg, Zitao Li +2
Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency o…