  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.
