Research Article
PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis
@INPROCEEDINGS{10.1007/978-3-031-94445-1_2, author={Antonia Januszewicz and Daniela Medrano Guti\^{e}rrez and Nirajan Koirala and Jiachen Zhao and Jonathan Takeshita and Jaewoo Lee and Taeho Jung}, title={PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis}, proceedings={Security and Privacy in Communication Networks. 20th EAI International Conference, SecureComm 2024, Dubai, United Arab Emirates, October 28--30, 2024, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2026}, month={9}, keywords={Public key cryptosystems Lattice-based Cryptography Private Stream Aggregation}, doi={10.1007/978-3-031-94445-1_2} }- Antonia Januszewicz
Daniela Medrano Gutiérrez
Nirajan Koirala
Jiachen Zhao
Jonathan Takeshita
Jaewoo Lee
Taeho Jung
Year: 2026
PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis
SECURECOMM
Springer
DOI: 10.1007/978-3-031-94445-1_2
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.
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