Results 21 to 30 of about 166,795 (258)

Real-world trajectory sharing with local differential privacy [PDF]

Proceedings of the VLDB Endowment, 2021
Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared.
Cunningham, Teddy, Cormode, Graham, Ferhatosmanoglu, Hakan , Srivastava, Divesh   +3 more
openaire   +3 more sources

Local differential privacy for human-centered computing

EURASIP Journal on Wireless Communications and Networking, 2020
Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks.
Xianjin Fang, Qingkui Zeng, Gaoming Yang
doaj   +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

Fldp: Flexible Strategy For Local Differential Privacy

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022
Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is used with frequency oracles (FO) because it can protect each user's privacy and prevent leakage of sensitive information.
Zhao, Dan, Zhao, Suyun, Liu, Ruixuan, Li, Cuiping, Liang, Wenjuan, Chen, Hong   +5 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

Edge Local Differential Privacy for Dynamic Graphs

, 2023
AbstractHuge amounts of data are generated and shared in social networks and other network topologies. This raises privacy concerns when such data is not protected from leaking sensitive or personal information. Network topologies are commonly modeled through static graphs.
Paul, Sudipta, Salas, Julián, Torra, Vicenç   +2 more
openaire   +3 more sources

Estimating Numerical Distributions under Local Differential Privacy [PDF]

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, 2020
When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator. We study the problem of recovering the distribution over a numerical domain while satisfying LDP.
Li, Zitao, Wang, Tianhao, Lopuhaä-Zwakenberg, Milan, Skoric, Boris, Li, Ninghui   +4 more
openaire   +2 more sources

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

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