RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909214035673088 |
|---|---|
| author | Wang, Zuyan Tao, Jun Zou, Dika |
| author_facet | Wang, Zuyan Tao, Jun Zou, Dika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20914 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs Wang, Zuyan Tao, Jun Zou, Dika Cryptography and Security 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. |
| title | RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2405.20914 |