Welcome to the resource topic for 2024/1460
Title:
PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis
Authors: Antonia Januszewicz, Daniela Medrano Gutierrez, Nirajan Koirala, Jiachen Zhao, Jonathan Takeshita, Jaewoo Lee, Taeho Jung
Abstract:Modern data analytics requires computing functions on streams of data points from many users that are challenging to calculate, due to both the high scale and nontrivial nature of the computation at hand. The need for data privacy complicates this matter further, as general-purpose privacy-enhancing technologies face limitations in at least scalability or utility. Existing work has attempted to improve this by designing purpose-built protocols for the use case of Private Stream Aggregation; however, prior work lacks the ability to compute more general aggregative functions without the assumption of trusted parties or hardware.
In this work, we present PPSA, a protocol that performs Private Polynomial Stream Aggregation, allowing the private computation of any polynomial function on user data streams even in the presence of an untrusted aggregator. Unlike previous state-of-the-art approaches, PPSA enables secure aggregation beyond simple summations without relying on trusted hardware; it utilizes only tools from cryptography and differential privacy. Our experiments show that PPSA has low latency during the encryption and aggregation processes with an encryption latency of 10.5 ms and aggregation latency of 21.6 ms for 1000 users, which are up to 138$\times$ faster than the state-of-the-art prior work.
ePrint: https://eprint.iacr.org/2024/1460
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