Abstract Private Stream Aggregation (PSA) protocols perform secure aggregation of time-
series data without leaking information about users' inputs to the aggregator. Previous work …

The massive scale and performance demands of privacy-preserving data aggregation make
integration of security and privacy difficult. Traditional tools in private computing are not well …

Abstract Outsourcing Machine Learning (ML) tasks to cloud servers is a cost-effective
solution when dealing with distributed data. However, outsourcing these tasks to cloud …

With the rapid advancements in machine learning and the widespread adoption of Model-as-
a-Service (MaaS) platforms, there has been significant attention on convolutional neural …

We build our secure multiparty computation (MPC) protocols on top of the fully homomorphic
encryption (FHE) scheme, BFVrns, and augment it with Proxy Re-Encryption (PRE). We offer …

Homomorphic encryption (HE) is a promising approach for privacy-preserving applications,
enabling a third party to assess functions on encrypted data. However, problems persist in …

In the modern era of outsourced computing, guaranteeing user data security and privacy on
data in use is important for ensuring user trust and utilization. Both purely cryptographic and …

Over the past decade, machine learning (ML) has experienced rapid progress, extending
from traditional image recognition tasks to advanced applications such as healthcare …

I propose to develop a quantitative and qualitative framework to integrate a Trusted
Execution Environment (TEE) into the pipeline of secure computation by combining it with …