Multi-Functional Privacy-Preserving Data Aggregation

Abstract

In practice, many applications like traffic monitoring and smart grids rely on computing functions on privacy-sensitive data. In order to protect privacy-sensitive data and still keep the ability to compute any arbitrary function, multi-functional privacy-preserving data aggregation schemes have been created. These schemes, however, can be abused by malicious users, leading to incorrect results. Existing literature mostly provides aggregation schemes which are either multi-functional or support malicious user detection, but to the best of our knowledge, there is only one scheme that provides both. This scheme requires each user to send a number of ciphertexts linear in the size of the aggregation function's domain. Furthermore, that scheme is not collusion-resistant. In this thesis, we design a multi-functional privacy-preserving data aggregation scheme with malicious user detection. In contrast to existing schemes in literature, the amount of messages is independent of the size of the aggregation function's domain, it does not rely on a trusted authority and it is collusion-resistant as long as at least two users are honest-but-curious.