Abstract
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 trans-formations 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000494008Publication status
publishedBook title
Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)Pages / Article No.
Publisher
USENIX AssociationEvent
Organisational unit
09653 - Paterson, Kenneth / Paterson, Kenneth
03757 - Roscoe, Timothy / Roscoe, Timothy
Funding
186050 - Privacy Preserving Federated Learning (SNF)
ETH-11 20-2 - Cryptographic Enforcement for End-to-End Data Privacy (ETHZ)
Notes
Conference lecture held on July 15, 2021More
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