activity
20192021
most citedLearning Privately over Distributed Features: An ADMM Sharing Approach

32 citations · 58 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CR20211 cited

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis

Zhilong Wang, Li Yu, Suhang Wang +1

A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage,…

cs.CR202113 cited

Security and Privacy for Artificial Intelligence: Opportunities and Challenges

Ayodeji Oseni, Nour Moustafa, Helge Janicke +3

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securi…

cs.CR20212 cited

Analyzing the Overhead of Filesystem Protection Using Linux Security Modules

Wenhui Zhang, Trent Jaeger, Peng Liu

Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing, and the count of the authorization hooks is nearly doubled. It is important to provide up-t…

cs.LG20201 cited

Towards classification parity across cohorts

Aarsh Patel, Rahul Gupta, Mukund Harakere +3

Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge a…

cs.CR20201 cited

Practical Verification of MapReduce Computation Integrity via Partial Re-execution

Eunjung Yoon, Peng Liu

Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted…

cs.LG201932 cited

Learning Privately over Distributed Features: An ADMM Sharing Approach

Yaochen Hu, Peng Liu, Linglong Kong +1

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem…