In this paper, we propose a privacy-preserving algorithm for aggregating data in multiple transactions from a large number of users at a thirdparty application. The aggregation is performed using the most commonly used weighted sum function. The new algorithm has several novel features. First, we propose a method to generate a privacy-assurance certificate that can be easily verified by all users without significant computation effort. In particular, the computational complexity of verification does not grow with the number of users. Second, the proposed approach has a very desirable feature that users do not have to directly communicate with each other. Instead, they only communicate with the application. These features distinguish our approach from the existing research in literature.
CITATION STYLE
Le, K., Ramanathan, P., & Saluja, K. K. (2014). Privacy assurances in multiple data-aggregation transactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8565, pp. 3–19). Springer Verlag. https://doi.org/10.1007/978-3-319-12160-4_1
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