Results 1 to 10 of about 1,840,684 (372)
Privacy-Preserving Blockchain Technologies
Sensors, 2023 The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps).
Dalton Cézane Gomes Valadares +4 more
doaj +4 more sources
Entropy, 2022 Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists.
Weiming Wei, Chunming Tang, Yucheng Chen
doaj +1 more source
Privacy‐preserving federated learning based on multi‐key homomorphic encryption [PDF]
International Journal of Intelligent Systems, 2021 With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data.
Jing Ma, Si-Ahmed Naas, S. Sigg, X. Lyu
semanticscholar +1 more source
Privacy-Preserving Bin-Packing With Differential Privacy
IEEE Open Journal of Signal Processing, 2022 With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service.
Tianyu Li +2 more
doaj +1 more source
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Comput. Biol. Medicine, 2023 There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services.
N. Khalid +4 more
semanticscholar +1 more source
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response [PDF]
Conference on Computer and Communications Security, 2014 Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees.
Ú. Erlingsson, A. Korolova, Vasyl Pihur
semanticscholar +1 more source
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation [PDF]
International Conference on Learning Representations, 2023 We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the private examples demonstrated in the prompt. We propose a novel algorithm that
Xinyu Tang +8 more
semanticscholar +1 more source
A Comprehensive Survey of Privacy-preserving Federated Learning
ACM Computing Surveys, 2021 The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results.
Xuefei Yin, Yanming Zhu, Jiankun Hu
semanticscholar +1 more source
Fairness and Privacy-Preserving in Federated Learning: A Survey [PDF]
Information Fusion, 2023 Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of
T. Rafi +3 more
semanticscholar +1 more source
Obfuscation for Privacy-preserving Syntactic Parsing [PDF]
, 2020 The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data.
Cohen, Shay B. +3 more
core +2 more sources