Zobrazeno 1 - 10
of 11 717
pro vyhledávání: '"differential privacy"'
Publikováno v:
Cybersecurity, Vol 7, Iss 1, Pp 1-19 (2024)
Abstract The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis
Externí odkaz:
https://doaj.org/article/e2d911a5e7c648a882329a6767368cd4
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 699-717 (2024)
Differential Privacy (DP) stands as a secure and efficient mechanism for privacy preservation, offering enhanced data utility without compromising computational complexity. Its adaptability is evidenced by its integration into blockchain-based Intern
Externí odkaz:
https://doaj.org/article/d193eed57c614630b16569448eefa708
Publikováno v:
EURASIP Journal on Information Security, Vol 2024, Iss 1, Pp 1-14 (2024)
Abstract In spatial crowdsourcing services, the trajectories of the workers are sent to a central server to provide more personalized services. However, for the honest-but-curious servers, it also poses a challenge in terms of potential privacy leaka
Externí odkaz:
https://doaj.org/article/58c735a68111413ca2643ad140fd2029
Publikováno v:
Tongxin xuebao, Vol 45, Pp 1-19 (2024)
In response to the challenge of comprehensively assessing privacy-preserving algorithms, an assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram was proposed, achieving a multi-perspective
Externí odkaz:
https://doaj.org/article/5f122502b29b446cb4c439ba24370998
Publikováno v:
智慧农业, Vol 6, Iss 4, Pp 149-159 (2024)
ObjectiveRice plays a crucial role in daily diet. The rice industry involves numerous links, from paddy planting to the consumer's table, and the integrity of the quality control data chain directly affects the credibility of rice quality control and
Externí odkaz:
https://doaj.org/article/4c92b4eaa1b9446ab28b5da5376f55f7
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract Background Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engag
Externí odkaz:
https://doaj.org/article/e8b58c63e214472cb570a208b150005f
Publikováno v:
Proceedings on Engineering Sciences, Vol 6, Iss 2, Pp 601-612 (2024)
Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical a
Externí odkaz:
https://doaj.org/article/a39fceaaa3d74ab6a6a42a16fc1e02e6
Autor:
Rawia Ahmed, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Naif Khalaf Alshammari, Fatma Ali Hendaoui
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
Externí odkaz:
https://doaj.org/article/1ddf1635d7a946d0bcbdd1cf65eaff27
Publikováno v:
Intelligent Systems with Applications, Vol 23, Iss , Pp 200416- (2024)
Medical research plays a crucial role within scientific research. Technological advancements, especially those related to the rise of machine learning, pave the way for the exploration of medical issues that were once beyond reach. Unstructured textu
Externí odkaz:
https://doaj.org/article/1ce30d77e734406f8b749bab062c0b7a
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 14, Iss 3 (2024)
The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highlight lessons learned and roadblocks encountered, distilling them into inc
Externí odkaz:
https://doaj.org/article/e7cdd2364e394dc8a70572941683514e