Zeph: Cryptographic Enforcement of End-to-End Data Privacy
This addresses the need for strong privacy assurance and control for users in data analytics, offering a novel approach to prevent data breaches and policy violations.
The paper tackles the problem of ensuring user privacy in data analytics by introducing Zeph, a system that cryptographically enforces user-defined privacy policies on encrypted data, enabling real-time, large-scale data stream analytics with low overhead.
As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importance. Today, privacy controls are enforced by data curators with full access to data in the clear. However, a plethora of recent data breaches show that even widely trusted service providers can be compromised. Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. This motivates the need for a new approach to data privacy that can provide strong assurance and control to users. This paper presents Zeph, a system that enables users to set privacy preferences on how their data can be shared and processed. Zeph enforces privacy policies cryptographically and ensures that data available to third-party applications complies with users' privacy policies. Zeph executes privacy-adhering data transformations in real-time and scales to thousands of data sources, allowing it to support large-scale low-latency data stream analytics. We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. We develop a prototype of Zeph on Apache Kafka to demonstrate that Zeph can perform large-scale privacy transformations with low overhead.