arXiv subjects

Kevin Liao

Publications and source records attributed to Kevin Liao.

3 recordsLinked to original sources

arXiv2025

Data Traceability for Privacy Alignment

This paper offers a new privacy approach for the growing ecosystem of services -- ranging from open banking to healthcare -- dependent on sensitive personal data sharing between individuals and third parties. While these services offer significant benefits, individuals want control over their data, transparency regarding how their data is used, and accountability from third parties for misuse. However, existing legal and technical mechanisms are inadequate for supporting these needs. A comprehensive approach to the modern privacy challenges of accountable third-party data sharing requires a closer alignment of technical system architecture and legal institutional design. In order to achieve this privacy alignment, we extend traditional security threat modeling and analysis to encompass a broader range of privacy notions than has been typically considered. In particular, we introduce the concept of covert-accountability, which addresses the risk from adversaries that may act dishonestly but nevertheless face potential identification and legal consequences. As a concrete instance of this design approach, we present the OTrace protocol, designed to provide traceable, accountable, consumer-control in third-party data sharing ecosystems. OTrace empowers consumers with the knowledge of who has their data, what it is being used for, what consent or other legal terms apply, and whom it is being shared with. By applying our alignment framework, we demonstrate that OTrace's technical affordances can provide more confident, scalable regulatory oversight when combined with complementary legal mechanisms.

cs.CR↗

arXiv2020

A Probabilistic Separation Logic

Probabilistic independence is a useful concept for describing the result of random sampling---a basic operation in all probabilistic languages---and for reasoning about groups of random variables. Nevertheless, existing verification methods handle independence poorly, if at all. We propose a probabilistic separation logic PSL, where separation models probabilistic independence. We first give a new, probabilistic model of the logic of bunched implications (BI). We then build a program logic based on these assertions, and prove soundness of the proof system. We demonstrate our logic by verifying information-theoretic security of cryptographic constructions for several well-known tasks, including private information retrieval, oblivious transfer, secure multi-party addition, and simple oblivious RAM. Our proofs reason purely in terms of high-level properties, like independence and uniformity.

cs.PL↗

arXiv2019

Autonomous Haiku Generation

Artificial Intelligence is an excellent tool to improve efficiency and lower cost in many quantitative real world applications, but what if the task is not easily defined? What if the task is generating creativity? Poetry is a creative endeavor that is highly difficult to both grasp and achieve with any level of competence. As Rita Dove, a famous American poet and author states, "Poetry is language at its most distilled and most powerful." Taking Doves quote as an inspiration, our task was to generate high quality haikus using artificial intelligence and deep learning.

cs.CL↗