Privacy-Preserving Data Aggregation in IoTs: A Randomize-then-Shuffle Paradigm

Aiming at providing a more rigorous data safeguard for the Internet of Things (IoTs), this paper initiates the study of privacy-preserving data aggregation. We propose a "randomize-then-shuffle" paradigm, which can be generalized into a two-step procedure, that is, a noise addition step plus a random permutation step. More specially, we design an efficient randomizer, which carefully guides the Data Contributors (DCs) to choose the privacy level and obfuscates the truth to ensure local differential privacy. Then, a shuffler is employed to receive the noisy data from all DCs. After that, it breaks the correct linkage between the senders and the receivers by applying a random permutation. Extensive simulations are provided to explore the privacy-utility landscape of our proposed paradigm.