Security, privacy, & cryptography

公開日

Stars Can Tell: A Robust Method to Defend against GPS Spoofing Attacks using Off-the-shelf Chipset

Shinan Liu, Xiang Cheng, Hanchao Yang, Yuanchao Shu, Xiaoran Weng, Ping Guo, Kexiong Zeng, Gang Wang, Yaling Yang

USENIX Security | August 2021

公開日

Leakage of Dataset Properties in Multi-Party Machine Learning

Wanrong Zhang, Shruti Tople, Olga Ohrimenko

Usenix Security | August 2021

プロジェクト

公開日

Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning

Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak

Information Systems Frontiers | July 2021, 巻23(4): pp. 867-882

公開日

Anomaly Detection in Large Scale Networks with Latent Space Models

Wesley Lee, Tyler H. McCormick, Joshua Neil, Cole Sodja, Yanran Cui

Technometrics | July 2021, pp. 1-23

公開日

Post-Compromise Security in Self-Encryption

Betül Durak, Gwangbae Choi, Serge Vaudenay

ITC | July 2021

公開日

SEAL-Embedded: A Homomorphic Encryption Library for the Internet of Things

Deepika Natarajan, Wei Dai

IACR Transactions on Cryptographic Hardware and Embedded Systems | July 2021, 巻2021(3): pp. 756-779

公開日

Grey-box Extraction of Natural Language Models

Santiago Zanella-Béguelin, Shruti Tople, Andrew Paverd, Boris Köpf

International Conference on Machine Learning | July 2021

編集者: Marina Meila and Tong Zhang

プロジェクト

イベント

Microsoft Research Redmond Cryptography and Privacy Colloquium

July 1, 2021 – February 22, 2024 | Microsoft Research Redmond

The Microsoft Research Redmond Cryptography and Privacy Group invites researchers from around the world to visit the group and speak in our colloquium series. Unless otherwise listed, the colloquia are open to the public.

Front entry of Building 99 on Microsoft Campus

公開日

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu

International Conference on Machine Learning (ICML) | July 2021

グループ

Privacy in AI (PAI)

Privacy in AI group explores questions related to user privacy and confidentiality in machine learning.

Security, privacy, & cryptography

Verifying Rust cryptography in SymCrypt, from standards to code

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

Ire identifies another LOTUSLITE specimen

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

Stars Can Tell: A Robust Method to Defend against GPS Spoofing Attacks using Off-the-shelf Chipset

Leakage of Dataset Properties in Multi-Party Machine Learning

Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning

Anomaly Detection in Large Scale Networks with Latent Space Models

Post-Compromise Security in Self-Encryption

SEAL-Embedded: A Homomorphic Encryption Library for the Internet of Things

Grey-box Extraction of Natural Language Models

Microsoft Research Redmond Cryptography and Privacy Colloquium

Large Scale Private Learning via Low-rank Reparametrization

Privacy in AI (PAI)

Security, privacy, & cryptography

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