189 citations · 197 across the 11 of their papers we have counts for
6 papers · 1 filter
SGX-MR-Prot: Efficient and Developer-Friendly Access-Pattern Protection in Trusted Execution Environments
AKM Mubashwir Alam, Justin Boyce, Keke Chen
Trusted Execution Environments, such as Intel SGX, use hardware supports to ensure the confidentiality and integrity of applications against a compromised cloud system. However, si…
Making Your Program Oblivious: a Comparative Study for Side-channel-safe Confidential Computing
AKM Mubashwir Alam, Keke Chen
Trusted Execution Environments (TEEs) are gradually adopted by major cloud providers, offering a practical option of \emph{confidential computing} for users who don't fully trust p…
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…
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…
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…
Secure Computation of Top-K Eigenvectors for Shared Matrices in the Cloud
James Powers, Keke Chen
With the development of sensor network, mobile computing, and web applications, data are now collected from many distributed sources to form big datasets. Such datasets can be host…