HUT: Enabling High-UTility, Batched Queries under Differential Privacy Protection for Internet-of-Vehicles

by   Junyu Liu, et al.

The emerging trends of Internet-of-Vehicles (IoV) demand centralized servers to collect/process sensitive data with limited computational resources on a single vehicle. Such centralizations of sensitive data demand practical privacy protections. One widely-applied paradigm, Differential Privacy, can provide strong guarantees over sensitive data by adding noises. However, directly applying DP for IoV incurs significant challenges for data utility and effective protection. We observe that the key issue about DP-enabled protection in IoV lies in how to synergistically combine DP with special characteristics of IoV, whose query sequences are usually formed as unbalanced batches due to frequent interactions between centralized servers and edge vehicles. To this end, we propose HUT, a new algorithm to enable High UTility for DP-enabled protection in IoV. Our key insight is to leverage the inherent characteristics in IoV: the unbalanced batches. Our key idea is to aggregate local batches and apply Order Constraints, so that information loss from DP protection can be mitigated. We evaluate the effectiveness of HUT against the state-of-the-art DP protection mechanisms. The results show that HUT can provide much lower information loss by 95.69% and simultaneously enable strong mathematically-guaranteed protection over sensitive data.


page 1

page 2

page 3

page 4


Asymmetric Differential Privacy

Recently, differential privacy (DP) is getting attention as a privacy de...

Characterizing Differentially-Private Techniques in the Era of Internet-of-Vehicles

Recent developments of advanced Human-Vehicle Interactions rely on the c...

Tight Differential Privacy Blanket for Shuffle Model

With the recent bloom of focus on digital economy, the importance of per...

Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy

Spontaneous reporting systems (SRS) have been developed to collect adver...

On the (Im)Possibility of Estimating Various Notions of Differential Privacy

We analyze to what extent final users can infer information about the le...

DPXPlain: Privately Explaining Aggregate Query Answers

Differential privacy (DP) is the state-of-the-art and rigorous notion of...

Please sign up or login with your details

Forgot password? Click here to reset