illustration of lock, four asterisks and a shield with a fingerprint on it on a green gradient background

Security, privacy, & cryptography

Developing technologies that protect data, preserve privacy, and build trustworthy computing systems.

Diagram showing the process of verifying cryptographic code. An algorithm from a standard is converted into a formal specification, while Rust code is converted into a code model. The specification and code model are then compared through proof and verification steps.

Verifying Rust cryptography in SymCrypt, from standards to code

Three white line-style icons centered on a textured blue-to-green gradient background: a shield, a checkmark inside a circle, and an ID card with a user silhouette—representing security, verification, and digital identity.

Vega: Zero-knowledge proofs for digital identity in the age of AI

Project Ire | | three white line icons on an abstract purple background | greater than / less than icon, search icon, shield icon

Ire identifies another LOTUSLITE specimen

Social Reasoning Bench | four icons on a blue to green gradient | person icon, chat bubble icon, chart icon, checklist icon

SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests

현재 선택 항목

정렬 기준: 가장 최근 순

선택 해제

이 결과 내에서 검색

콘텐츠 유형

게시 날짜 표시

출판

Alleviating Privacy Attacks via Causal Learning

Shruti Tople, Amit Sharma, Aditya V. Nori

International Conference on Machine Learning (ICML) | July 2020

Github 프로젝트 프로젝트

출판

PACT: Privacy-Sensitive Protocols And Mechanisms for Mobile Contact Tracing

Justin Chan, Landon Cox, Dean Foster, Shyam Gollakota, Eric Horvitz, Joseph Jaeger, Sham Kakade, Tadayoshi Kohno, John Langford, Jonathan Larson, Puneet Sharma, Sudheesh Singanamalla, Jacob Sunshine, Stefano Tessaro

IEEE Data Engineering Bulletin | July 2020, 제 43 권(2): pp. 15-35

프로젝트

출판

Differentially Private Set Union

Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin

International Conference on Machine Learning 2020 | July 2020

Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).

Github 프로젝트

출판

Privately Learning Markov Random Fields

Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu

ICML 2020 | July 2020

프로젝트

출판

COVID19 Data Donation Survey Methodology Summary

Elissa M. Redmiles, John Krumm

MSR-TR-2020-22 | July 2020

글쓴이 Microsoft

출판

Locally Private Hypothesis Selection

Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

Conference on Learning Theory (COLT) 2020 | July 2020

프로젝트

툴

Differentially Private Set Union

This repository contains the code and dataset for the following paper: Differentially Private Set Union with Applications to Vocabulary Generation

깃허브 출판

출판

Modeling infection methods of computer malware in the presence of vaccinations using epidemiological models: an analysis of real-world data

Elad Yom-Tov, Nir Levy, Amir Rubin

International Journal of Data Science and Analytics | June 2020

프로젝트

출판

CacheQuery: learning replacement policies from hardware caches

Pepe Vila, Pierre Ganty, Marco Guarnieri, Boris Köpf

2020 Programming Language Design and Implementation | June 2020

동영상

Quantum-safe cryptography: Securing today’s data against tomorrow’s computers

June 4, 2020 | Christian Paquin

As the world prepares for the advent of the quantum computer, the security community must also prepare to defend against it. Most of the cryptography currently in use succumbs to quantum attacks. Although quantum computers…

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Security, privacy, & cryptography

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