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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
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 +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 +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
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
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Local Differential Privacy for Person-to-Person Interactions
IEEE Open Journal of the Computer Society, 2022 Currently, many global organizations collect personal data for marketing, recommendation system improvement, and other purposes. Some organizations collect personal data securely based on a technique known as $\epsilon$-local differential privacy (LDP ...
Yuichi Sei, Akihiko Ohsuga
doaj +1 more source
Combinational Randomized Response Mechanism for Unbalanced Multivariate Nominal Attributes
IEEE Access, 2020 At present, many enterprises provide users with better services by collecting their sensitive information. However, these enterprises will inevitably cause the leakage of users' information, thereby infringing on users' privacy.
Xuejie Feng +3 more
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