PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis
Journal
Security and Privacy in Communication Networks : 20th EAI International Conference, SecureComm 2024, Dubai, United Arab Emirates, October 28–30, 2024, Proceedings, Part I
ISSN
1867-8211
1867-822X
Publisher
Springer Nature Switzerland
Date Issued
2025
Author(s)
Januszewicz, Antonia
Medrano Gutiérrez, Daniela
Koirala, Nirajan
Zhao, Jiachen
Takeshita, Jonathan
Lee, Jaewoo
Jung, Taeho
Type
text::book::book part
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. ©The authors ©Springer.
License
Acceso Restringido
How to cite
Januszewicz, A. et al. (2026). PPSA: Polynomial Private Stream Aggregation for Time-Series Data Analysis. In: Alrabaee, S., Choo, KK.R., Damiani, E., Deng, R.H. (eds) Security and Privacy in Communication Networks. SecureComm 2024. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-031-94445-1_2