Results 21 to 30 of about 7,877,596 (288)

A Comprehensive Survey on Local Differential Privacy [PDF]

Security and Communication Networks, 2020
With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In
Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu   +4 more
openaire   +1 more source

Local Differential Privacy for Belief Functions

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
In this paper, we propose two new definitions of local differential privacy for belief functions. One is based on Shafer’s semantics of randomly coded messages and the other from the perspective of imprecise probabilities. We show that such basic properties as composition and post-processing also hold for our new definitions.
Qiyu Li, Chunlai Zhou, Biao Qin, Zhiqiang Xu   +3 more
openaire   +3 more sources

A multimodal differential privacy framework based on fusion representation learning [PDF]

, 2022
Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of ...
Chaoxin Cai, Sang, Yingpeng, Hui Tian, Cai, Chaoxin, Tian, Hui, Yingpeng Sang   +5 more
core   +1 more source

Utility-optimized Local Differential Privacy Joint Distribution Estimation Mechanisms [PDF]

Jisuanji kexue, 2023
Compared with traditional centralized differential privacy,local differential privacy(LDP) has the advantage of not re-lying on trusted third parties,but it also has the problem of low data utility.The utility-optimized local differential privacy(ULDP ...
YIN Shiyu, ZHU Youwen, ZHANG Yue
doaj   +1 more source

A Shuffling Framework for Local Differential Privacy

CoRR, 2021
ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity and subsequently, auxiliary information using the order of the data. An alternative model, shuffle DP, prevents this by shuffling the noisy responses uniformly at random.
Casey Meehan, Amrita Roy Chowdhury 0001, Kamalika Chaudhuri, Somesh Jha   +3 more
openaire   +2 more sources

Privacy Preservation in the Internet of Vehicles using Local Differential Privacy and IOTA Ledger

, 2023
With the growth in Vehicular Ad Hoc Network (VANET) technology, many vehicular devices are communicating with each other and with the edge nodes, generating a massive amount of data. One of the biggest challenges is to preserve users’ privacy as the data
Khan, A., Anjum, A., Jeon, G., Iftikhar, Z., Shah, M. A.   +4 more
core   +1 more source

Local Differential Privacy for Federated Learning

, 2022
17 ...
Mahawaga Arachchige Pathum Chamikara, Dongxi Liu, Seyit Camtepe, Surya Nepal, Marthie Grobler, Peter Bertók, Ibrahim Khalil 0001   +6 more
openaire   +2 more sources

RCP:Mean Value Protection Technology Under Local Differential Privacy [PDF]

Jisuanji kexue, 2023
This paper mainly focuses on the mean estimation problem in differential privacy query.After introducing the current mainstream local differential privacy design scheme of numerical data mean estimation,it first introduces the random censoring mechanism ...
LIU Likang, ZHOU Chunlai
doaj   +1 more source

Review of Differential Privacy Research [PDF]

Jisuanji kexue, 2023
In the past decade,widespread data collection has become the norm.With the rapid development of large-scale data analysis and machine learning,data privacy is facing fundamental challenges.Exploring the trade-offs between privacy protection and data ...
ZHAO Yuqi, YANG Min
doaj   +1 more source

A secure and privacy preserved infrastructure for VANETs based on federated learning with local differential privacy

, 2023
Advancements in Vehicular ad-hoc Network (VANET) technology have led to a growing network of interconnected devices, including edge devices, resulting in substantial data generation.
Khan, A., Anjum, A., Batool, H., Izzo, S., Mazzocca, C., Jeon, G., Stefano Izzo, Batool, Hajira, Abid Khan, Adeel Anjum, Hajira Batool, Jeon, Gwanggil, Carlo Mazzocca, Gwanggil Jeon, Mazzocca, Carlo, Izzo, Stefano, Khan, Abid, Anjum, Adeel   +17 more
core   +1 more source