Security, privacy, and cryptography

Publication

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

Publication

Leakage of Dataset Properties in Multi-Party Machine Learning

Wanrong Zhang, Shruti Tople, Olga Ohrimenko

Usenix Security | August 2021

Project

Publication

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, Vol 23(4): pp. 867-882

Publication

Post-Compromise Security in Self-Encryption

Betül Durak, Gwangbae Choi, Serge Vaudenay

ITC | July 2021

Publication

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

Publication

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, Vol 2021(3): pp. 756-779

Publication

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

Publication

Grey-box Extraction of Natural Language Models

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

International Conference on Machine Learning | July 2021

Editor(s): Marina Meila and Tong Zhang

Project

Event

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

Group

Privacy in AI (PAI)

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

Security, privacy, and cryptography

Leveraging Large Language Models for Automated Proof Synthesis in Rust

Intern Insights: Dr. Josh Benaloh with Anunay Kulshrestha and Karan Newatia

Closed-Form Bounds for DP-SGD against Record-level Inference

ProvCam: A Camera Module with Self-Contained TCB for Producing Verifiable Videos

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

Post-Compromise Security in Self-Encryption

Anomaly Detection in Large Scale Networks with Latent Space Models

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

Large Scale Private Learning via Low-rank Reparametrization

Grey-box Extraction of Natural Language Models

Microsoft Research Redmond Cryptography and Privacy Colloquium

Privacy in AI (PAI)

Security, privacy, and cryptography

Highlights