Results 31 to 40 of about 40,084 (262)

Privacy-preserving Prediction

CoRR, 2018
Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privacy.
Cynthia Dwork, Vitaly Feldman
openaire   +3 more sources

Privacy-preserving machine learning based on secure three-party computations

Безопасность информационных технологий, 2022
The paper is devoted to the analysis of privacy-preserving machine learning systems based on the concept of secure three-party computations. After general information about the purposes of secure multi-party computations and privacy-preserving machine ...
Sergey V. Zapechnikov
doaj   +1 more source

PEIGEN – a Platform for Evaluation, Implementation, and Generation of S-boxes

IACR Transactions on Symmetric Cryptology, 2019
In this paper, a platform named PEIGEN is presented to evaluate security, find efficient software/hardware implementations, and generate cryptographic S-boxes.
Zhenzhen Bao, Jian Guo, San Ling, Yu Sasaki   +3 more
doaj   +1 more source

Privacy Preserving Clustering [PDF]

, 2005
The freedom and transparency of information flow on the Internet has heightened concerns of privacy. Given a set of data items, clustering algorithms group similar items together. Clustering has many applications, such as customerbehavior analysis, targeted marketing, forensics, and bioinformatics. In this paper, we present the design and analysis of a
Somesh Jha, Louis Kruger, Patrick D. McDaniel   +2 more
openaire   +1 more source

Privacy Preserving Data Mining [PDF]

Journal of Cryptology, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yehuda Lindell, Benny Pinkas
openaire   +2 more sources

Privacy-Preserving Loyalty Programs [PDF]

, 2015
Presented at the 9th DPM International Workshop on Data Privacy Management (DPM 2014, held on Sep. 10, 2014).
Alberto Blanco-Justicia, Josep Domingo-Ferrer   +1 more
openaire   +2 more sources

Privacy Computing: Concept, Computing Framework, and Future Development Trends

Engineering, 2019
With the rapid development of information technology and the continuous evolution of personalized services, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the
Fenghua Li, Hui Li, Ben Niu, Jinjun Chen
doaj   +1 more source

Deconvoluting kernel density estimation and regression for locally differentially private data

Scientific Reports, 2020
Local differential privacy has become the gold-standard of privacy literature for gathering or releasing sensitive individual data points in a privacy-preserving manner.
Farhad Farokhi
doaj   +1 more source

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies

Nature Communications
Genome-wide association studies help uncover genetic influences on complex traits and diseases. Importantly, multi-site data collaborations enhance the statistical power of these studies but pose challenges due to the sensitivity of genomic data ...
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal, Nico Pfeifer, Mete Akgün   +4 more
doaj   +1 more source

Secure Federated Evolutionary Optimization—A Survey

Engineering
With the development of edge devices and cloud computing, the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past decade.
Qiqi Liu, Yuping Yan, Yaochu Jin, Xilu Wang, Peter Ligeti, Guo Yu, Xueming Yan   +6 more
doaj   +1 more source