Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Blanton, Marina"'
Publikováno v:
Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2023, No. 3, pp. 432-445, 2023
Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achi
Externí odkaz:
http://arxiv.org/abs/2312.10247
Although Secure Multiparty Computation (SMC) has seen considerable development in recent years, its use is challenging, resulting in complex code which obscures whether the security properties or correctness guarantees hold in practice. For this reas
Externí odkaz:
http://arxiv.org/abs/2306.00308
Secure multi-party computation has seen substantial performance improvements in recent years and is being increasingly used in commercial products. While a significant amount of work was dedicated to improving its efficiency under standard security m
Externí odkaz:
http://arxiv.org/abs/2209.10457
Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Results: We describe general and scalable strategy to build machine learnin
Externí odkaz:
http://arxiv.org/abs/1806.06477
We present three private fingerprint alignment and matching protocols, based on what are considered to be the most precise and efficient fingerprint recognition algorithms, which use minutia points. Our protocols allow two or more honest-but-curious
Externí odkaz:
http://arxiv.org/abs/1702.03379
Autor:
DeBenedetto, Justin, Blanton, Marina
Growth in research collaboration has caused an increased need for sharing of data. However, when this data is private, there is also an increased need for maintaining security and privacy. Secure multi-party computation enables any function to be sec
Externí odkaz:
http://arxiv.org/abs/1612.08678
Recent compilers allow a general-purpose program (written in a conventional programming language) that handles private data to be translated into secure distributed implementation of the corresponding functionality. The resulting program is then guar
Externí odkaz:
http://arxiv.org/abs/1509.01763
This paper studies a discrepancy-sensitive approach to dynamic fractional cascading. We provide an efficient data structure for dominated maxima searching in a dynamic set of points in the plane, which in turn leads to an efficient dynamic data struc
Externí odkaz:
http://arxiv.org/abs/0904.4670
Autor:
Douglas, Alecia C., Mills, Juline E., Niang, Mamadou, Stepchenkova, Svetlana, Byun, Sookeun, Ruffini, Celestino, Lee, Seul Ki, Loutfi, Jihad, Lee, Jung-Kook, Atallah, Mikhail, Blanton, Marina
Publikováno v:
In Computers in Human Behavior 2008 24(6):3027-3044
Publikováno v:
International Journal of Information Security. Nov2017, Vol. 16 Issue 6, p577-601. 25p.