189 citations · 192 across the 6 of their papers we have counts for
6 papers
A Comparative Study of Image Disguising Methods for Confidential Outsourced Learning
Sagar Sharma, Yuechun Gu, Keke Chen
Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale co…
Confidential Machine Learning on Untrusted Platforms: A Survey
Sagar Sharma, Keke Chen
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges,…
SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications
A K M Mubashwir Alam, Sagar Sharma, Keke Chen
Intel SGX has been a popular trusted execution environment (TEE) for protecting the integrity and confidentiality of applications running on untrusted platforms such as cloud. Howe…
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning
Sagar Sharma, Keke Chen
Deep learning model developers often use cloud GPU resources to experiment with large data and models that need expensive setups. However, this practice raises privacy concerns. Ad…
Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems
Sagar Sharma, Keke Chen, Amit Sheth
Modern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data…
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
Sagar Sharma, Keke Chen
User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed…