We present TVA, a multi-party computation (MPC) system for secure analytics on secret-
shared time series data. TVA achieves strong security guarantees in the semi-honest and …

In this work we revisit the problem of using general-purpose MPC schemes to emulate the
trusted dataholder in differential privacy (DP), to achieve the same accuracy but without the …

In the last fifteen years, there has been a steady stream of works combining differential
privacy with various other cryptographic disciplines, particularly that of multi-party …

Recent advancements in transformers have revolutionized machine learning, forming the
core of Large language models (LLMs). However, integrating these systems into everyday …

In the business scenarios at Ant Group, there is a rising demand for collaborative data
analysis among multiple institutions, which can promote health insurance, financial services …

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency,
and reliability in increasingly volatile energy grids. However, data from smart meters can …

Traditional notions of secure multiparty computation (MPC) allow mutually distrusting parties
to jointly compute a function over their private inputs, but typically do not specify how these …

In this paper, we design and present a novel model called SePEnTra to ensure the security
and privacy of energy data while sharing with other entities during energy trading to …

Blockchain technologies have introduced a compelling paradigm for a new understanding of
security through decentralized networks and consensus mechanisms. However, they need …

Code-based cryptography is a promising candidate for post-quantum cryptography due to its
strong security guarantees and efficient implementations. In this paper, we explore the use …