Results 31 to 40 of about 1,916,192 (291)
Learning With Differential Privacy [PDF]
, 2020 The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized ...
Poushali Sengupta +2 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. +4 more
core +1 more source
Differential Privacy in Federated Learning: Challenges and Prospects [PDF]
Jisuanji kexue yu tansuoIn federated learning, differential privacy serves as a key technology for addressing privacy concerns, yet it still faces multiple challenges in adapting to heterogeneous environments, personalized privacy design, and communication optimization ...
LIU Yi, JIANG Chengjie, YANG Songtao, ZHANG Lei, WU Shiwei
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
doaj +1 more source
FL-ODP: An Optimized Differential Privacy Enabled Privacy Preserving Federated Learning
IEEE Access, 2023 Privacy-preserving methods and techniques aim to safeguard the privacy of individuals and groups while facilitating data sharing for specific purposes.
Maria Iqbal +4 more
doaj +1 more source
Differential Privacy in Practice: Expose your Epsilons!
The Journal of Privacy and Confidentiality, 2019 Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn ...
Cynthia Dwork +2 more
doaj +1 more source
Lemmas of Differential Privacy
CoRR, 2022 We aim to collect buried lemmas that are useful for proofs. In particular, we try to provide self-contained proofs for those lemmas and categorise them according to their usage.
Yiyang Huang, Clément L. Canonne
openaire +3 more sources
Boosting and Differential Privacy [PDF]
2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010 Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the ...
Dwork, Cynthia +2 more
openaire +2 more sources
Differential privacy: an information-theoretic approach to preserve privacy in datasets
, 2022 reservedIn large datasets, anonymization may be not enough to preserve privacy. In recent years to tackle privacy preservation in datasets, it has been proposed a mathematical approach called differential privacy, which is the topic of this thesis.
FABRIS, GIULIA
core
Enhancing Metric Privacy With a Shuffler
International audienceDifferential Privacy (DP) is one of the most successful privacy-preserving frameworks. In the central model of DP a trusted server adds controlled noise as it acts as an interface between the data providers (users) and the data ...
Chatzikokolakis, Konstantinos +2 more
core +6 more sources