Results 1 to 10 of about 27,791 (249)

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, Hang Jiang, Yange Chen, Baocang Wang, Le Gao   +4 more
doaj   +4 more sources

Mechanisms for Robust Local Differential Privacy [PDF]

Entropy
We 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, Jasper Goseling   +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

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, Baichuan Wang, Jinsheng Li, Yi Hua, Shaobo Zhang   +4 more
doaj   +2 more sources

Sequential Change Detection with Local Differential Privacy [PDF]

Entropy
Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false alarm rate when pre- and post-change distributions are fully known.
Lixing Zhang, Xuran Liu, Ruizhi Zhang, Liyan Xie   +3 more
doaj   +2 more sources

Safeguarding cross-silo federated learning with local differential privacy

Digital Communications and Networks, 2022
Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the clients.
Chen Wang, Xinkui Wu, Gaoyang Liu, Tianping Deng, Kai Peng, Shaohua Wan   +5 more
doaj   +3 more sources

IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

International Journal of Distributed Sensor Networks, 2020
Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which ...
Hui Cao, Shubo Liu, Renfang Zhao, Xingxing Xiong   +3 more
doaj   +2 more sources

ALDP-FL for adaptive local differential privacy in federated learning [PDF]

Scientific Reports
Federated 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

A Privacy Preserving Framework for Worker’s Location in Spatial Crowdsourcing Based on Local Differential Privacy

Future Internet, 2018
With the development of the mobile Internet, location-based services are playing an important role in everyday life. As a new location-based service, Spatial Crowdsourcing (SC) involves collecting and analyzing environmental, social, and other ...
Jiazhu Dai, Keke Qiao
doaj   +3 more sources

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis [PDF]

Sensors, 2020
Xinyu Yang, Xuefeng Zhang, Teng Wang
exaly   +2 more sources