Work

Privacy heroes need data disguises

Year: 2021

Type: conference-paper

Abstract: Providing privacy in complex, data-rich applications is hard. Deleting accounts, anonymizing an account's contributions, and other privacy-related actions may require the traversal and transformation of interwoven state in a relational database. Finding the affected data is already nontrivial, but privacy actions must additionally balance competing requirements, such as preserving data trails for legal reasons or allowing users to change their mind. We believe a systematic shared framework for specifying and implementing privacy transformations could simplify and empower applications. Our prototype, data disguising, supports fine-grained, nuanced, and useful policies that would be cumbersome to implement manually, including reversible transformations that can compose.

Source: Workshop on Hot Topics in Operating Systems

DOI: 10.1145/3458336.3465284

Authors: Lillian Tsai, Malte Schwarzkopf, Eddie Kohler

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Brown University, Harvard University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: CSR: Medium: Collaborative Research: Soup: Flexible Storage and Processing for On-Line Applications, CSR: Medium: Collaborative Research: Soup: Flexible Storage and Processing for On-Line Applications, CAREER: Privacy-Compliant Web Services By Construction