RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs

TL;DR

RASE introduces a novel privacy-preserving data aggregation method for IoT devices that combines noise addition, random permutation, and approximate estimation to enhance privacy protection against disclosure attacks.

Contribution

The paper proposes a new three-step RASE paradigm that improves privacy in IoT data aggregation by integrating differential privacy and data shuffling techniques.

Findings

RASE effectively balances privacy and utility in IoT data aggregation.

Simulations demonstrate RASE's robustness against disclosure attacks.

The method achieves high privacy guarantees with acceptable data accuracy.

Abstract

The growing popular awareness of personal privacy raises the following quandary: what is the new paradigm for collecting and protecting the data produced by ever-increasing sensor devices. Most previous studies on co-design of data aggregation and privacy preservation assume that a trusted fusion center adheres to privacy regimes. Very recent work has taken steps towards relaxing the assumption by allowing data contributors to locally perturb their own data. Although these solutions withhold some data content to mitigate privacy risks, they have been shown to offer insufficient protection against disclosure attacks. 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 novel paradigm (called RASE), which can be generalized into a 3-step sequential procedure, noise addition, followed by random permutation, and then parameter estimation. Specially, we design a differentially private randomizer, which carefully guides data contributors to obfuscate the truth. Then, a shuffler is employed to receive the noisy data from all data contributors. After that, it breaks the correct linkage between senders and receivers by applying a random permutation. The estimation phase involves using inaccurate data to calculate an approximate aggregate value. Extensive simulations are provided to explore the privacy-utility landscape of our RASE.