Results 31 to 40 of about 7,877,596 (288)

Successive Point-of-Interest Recommendation With Local Differential Privacy

IEEE Access, 2021
A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations.
Jong Seon Kim, Jong Wook Kim, Yon Dohn Chung   +2 more
doaj   +1 more source

Local differential privacy-based frequent sequence mining

Journal of King Saud University: Computer and Information Sciences, 2022
Frequent sequence mining (FSM) is a fundamental component for analyzing sequential data in the big data era. However, collecting and analyzing sequence data incurs serious privacy issues for users.
Teng Wang, Zhi Hu
doaj   +1 more source

Comparing Local and Central Differential Privacy Using Membership Inference Attacks

, 2021
Part 1: Differential PrivacyInternational audienceAttacks that aim to identify the training data of neural networks represent a severe threat to the privacy of individuals in the training dataset.
Grassal, Philip, W., Florian Kerschbaum, Schneider, Steffen, Jonas Robl, Kerschbaum, Florian, Daniel Bernau, Philip W. Grassal, Steffen Schneider, Robl, Jonas, Bernau, Daniel   +9 more
core   +1 more source

Local Differential Privacy for Bayesian Optimization

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee. Specifically, the rewards from each user are further corrupted to protect privacy and the learner only has access to the
Xingyu Zhou 0001, Jian Tan
openaire   +3 more sources

Local Differential Privacy Graph Data Modeling Method for Link Prediction

Journal of Harbin University of Science and Technology, 2023
To solve the problem of node sensitive link privacy being exposed in the process of link prediction on industrial business graph data , according to the theory of local differential privacy , the shortcomings of the existing graph privacy protection ...
HANQilong, WUXiaoming
doaj   +1 more source

The Privacy-Utility Tradeoff of Robust Local Differential Privacy

CoRR, 2021
We consider data release protocols for data $X=(S,U)$, where $S$ is sensitive; the released data $Y$ contains as much information about $X$ as possible, measured as $\operatorname{I}(X;Y)$, without leaking too much about $S$. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy.
Milan Lopuhaä-Zwakenberg, Jasper Goseling   +1 more
openaire   +3 more sources

Local Differential Privacy Location Protection for Mobile Terminals Based on Huffman Coding [PDF]

Jisuanji kexue yu tansuo
The location information of mobile terminals is closely linked to personal privacy, which may threaten users’ life and property safety if leaked. Local differential privacy model provides strict privacy protection effect, allows users to handle and ...
YAN Yan, LYU Yaqin, LI Feifei
doaj   +1 more source

Frequency Estimation of Evolving Data Under Local Differential Privacy

, 2023
International audienceCollecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential privacy (LDP) protocols, which ensure the privacy ...
Arcolezi, Héber Hwang, Pinzón, Carlos, Gambs, Sébastien, Palamidessi, Catuscia   +3 more
core   +1 more source

Context-Aware Local Differential Privacy

CoRR, 2019
Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-
Jayadev Acharya, Kallista A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun   +4 more
openaire   +3 more sources

Multi-level local differential privacy algorithm recommendation framework

Tongxin xuebao, 2022
Local differential privacy (LDP) algorithm usually assigned the same protection mechanism and parameters to different users.However, it ignored the differences among the device resources and the privacy requirements of different users.For this reason, a ...
Hanyi WANG, Xiaoguang LI, Wenqing BI, Yahong CHEN, Fenghua LI, Ben NIU   +5 more
doaj   +2 more sources