Results 1 to 10 of about 166,795 (258)
Mechanisms for Robust Local Differential Privacy [PDF]
EntropyWe consider privacy mechanisms for releasing data X=(S,U), where S is sensitive and U is non-sensitive. We introduce the robust local differential privacy (RLDP) framework, which provides strong privacy guarantees, while preserving utility.
Milan Lopuhaä-Zwakenberg +1 more
doaj +4 more sources
PLDP-FL: Federated Learning with Personalized Local Differential Privacy [PDF]
Entropy, 2023 As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection.
Xiaoying Shen +4 more
doaj +4 more sources
Hierarchical Aggregation for Numerical Data under Local Differential Privacy [PDF]
Sensors, 2023 The proposal of local differential privacy solves the problem that the data collector must be trusted in centralized differential privacy models. The statistical analysis of numerical data under local differential privacy has been widely studied by many ...
Mingchao Hao, Wanqing Wu, Yuan Wan
doaj +2 more sources
Marginal Release Under Local Differential Privacy [PDF]
Proceedings of the 2018 International Conference on Management of Data, 2017 Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects.
Bassily R. +8 more
core +2 more sources
Local differential privacy protection for wearable device data. [PDF]
PLoS ONE, 2022 Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services.
Zhangbing Li +4 more
doaj +2 more sources
Manipulation Attacks in Local Differential Privacy [PDF]
The Journal of Privacy and Confidentiality, 2021 Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems.
Albert Cheu, Adam Smith, Jonathan Ullman
doaj +6 more sources
Local Differential Privacy for Evolving Data
The Journal of Privacy and Confidentiality, 2020 There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use.
Matthew Joseph +3 more
doaj +4 more sources
Extremal Mechanisms for Local Differential Privacy [PDF]
, 2015 Local differential privacy has recently surfaced as a strong measure of privacy in contexts where personal information remains private even from data analysts.
Kairouz, Peter +2 more
core +2 more sources
ALDP-FL for adaptive local differential privacy in federated learning [PDF]
Scientific ReportsFederated learning, as an emerging distributed learning framework, enables model training without compromising user data privacy. However, malicious attackers may still infer sensitive user information by analyzing model updates during the federated ...
Lixin Cui, Xu Wu
doaj +2 more sources
Fisher Information Under Local Differential Privacy [PDF]
IEEE Journal on Selected Areas in Information Theory, 2020 We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon$ under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the ...
Leighton Pate Barnes +2 more
openaire +2 more sources