Results 31 to 40 of about 1,822,892 (288)
Liftings for Differential Privacy
CoRR, 2017 Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. There are two styles of definitions for this construction. Earlier definitions require the existence of one or more witness distributions, while a recent definition
Gilles Barthe +4 more
openaire +5 more sources
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-Preserving Monotonicity of Differential Privacy Mechanisms
Applied Sciences, 2018 Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and ...
Hai Liu +5 more
doaj +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
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 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
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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
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