Deep learning (DL)-powered personalization holds great promise to fundamentally transform the way people live, work and travel, but poses high risk to people’s individual privacy. This project will address the privacy risks arising in DL-powered contextual mobile services by developing solutions that facilitate the use of personal information while maintaining explicit user control over the use of such information.
In particular, we will build PADLOCK, a Privacy-Aware Deep Learning Of Contextual Knowledge engine. PADLOCK executes DL computation over users’ personal data in a sandbox environment, while performing lightweight static and runtime analysis to ensure that mobile apps comply with users’ privacy policies. The design of PADLOCK explores the tradeoff among privacy protection, communication cost, system overhead and service quality, providing solutions with different provable privacy and efficiency features for a wide range of contextual mobile services.
Integration of Static and Dynamic Code Stylometry Analysis for Programmer De-anonymization [pdf]
Ningfei Wang, Shouling Ji, Ting Wang
The 11th ACM Workshop on Artificial Intelligence and Security (AISec '18)
🏅Best Paper Award
Backdoor Attacks against Learning Systems [pdf, bibtex]
Yujie Ji, Xinyang Zhang, Ting Wang
The 5th IEEE Conference on Communications and Network Security (CNS '17)
Private, yet Practical, Multiparty Deep Learning
Xinyang Zhang, Shouling Ji, Hui Wang, Ting Wang
The 37th IEEE International Conference on Distributed Computing Systems (ICDCS '17)
De-SAG: De-anonymizing Structure-Attribute Graph Data
Shouling Ji, Shukun Yang, Ting Wang, Prateek Mittal, Raheem Beyah
IEEE Transactions on Dependable and Secure Computing (TDSC), 2017
Differentially Private Releasing via Deep Generative Models
Xinyang Zhang, Shouling Ji, Ting Wang
Quantifying Graph Anonymity, Utility, and De-anonymity [pdf, bibtex]
Shouling Ji, Tianyu Du, Zhen Hong, Ting Wang, Raheem Beyah
2018 IEEE International Conference on Computer Communications (INFOCOM '18)
We are grateful for the National Science Foundation (NSF) and Nvidia to support our research.