A verifiable and privacy‐preserving multidimensional data aggregation scheme in mobile crowdsensing

Abstract

Mobile crowdsensing has become a popular data collection paradigm and has been extensively studied. Since the analysis of sensory data usually reveals privacy of mobile users, data aggregation technology is widely used to avoid privacy disclosure. However, traditional privacy‐preserving data aggregation schemes cannot provide aggregate statistics of multidimensional data in fine‐grained areas or resist collusive attacks. Moreover, the cloud platform is not fully trusted, and it is challenging to verify the correctness of data aggregation results. To provide privacy‐preserving multidimensional statistics within fine‐grained areas and verify results of data aggregation, we propose a verifiable privacy‐preserving data aggregation scheme in this article, which is based on data double‐masking, Shamir's secret sharing, and bilinear mapping. Specifically, a key agreement is utilized to establish mask seeds for users, while the users are required to extend mask values and generate the verification key with bilinear mapping. The cloud platform aggregates masked data and provides proof of computational results. Theoretical analysis demonstrates our scheme can protect data and location privacy, verify the aggregation results, and improve the rubustness to failure. The simulation results show that the computational overhead of mobile devices by using the proposed anticollusion scheme is better than that of homomorphic encryption. In addition, the computational complexity of task requesters is less than that of the PVPA scheme for verification of multidimensional aggregation results.

Non-Interactive and Secure Data Aggregation Scheme for Internet of Things

“…Although the scheme proposes a verification algorithm for aggregating results, it does not support user drop out, and therefore, once the user drops out, this aggregation protocol will aggregate incorrect results. Based on Bonawitz's scheme, Jiang et al [21] added the functions of location privacy and multi-dimensional data analysis for mobile users, but the communication cost is still very high and most mobile users must be online.…”

“…In the previous schemes, the secret sharing recovery algorithm is usually used to restore the mask values of exiting users, which improves the robustness of their scheme. Some methods [12,21] require a large number of mobile users to be online, and their masking formula is y u = x u + PRG(b u ) + ∑ v∈U:u<v (PRG(s u,v ) − ∑ v∈U:u>v (PRG(s u,v ) mod R. The x u represents the private data of each user and b u is a random number generated by the user who distributes shares of b u to the other users, and s u,v is the agreement key between user u and user v. Assume that there are no user drops out, then the cloud server will correctly compute the aggregation result ∑ n u=1 (y u ), and then the ∑ n u=1 (∑ v∈U:u<v PRG(s u,v ) − ∑ v∈U:u>v PRG(s u,v )) will be zero. Once a user drops out, a large number of users are needed to recover the shared values b u and s u,v by the secret reconstruction algorithm to make ∑ n u=1 (∑ v∈U:u<v PRG(s u,v ) − ∑ v∈U:u>v PRG(s u,v )) zero, so a large number of users need to be online to ensure that the scheme is executed correctly.…”

Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

“…To solve these security problems, we introduce a double-masking scheme [39,40]. In the work [40], the double-masking scheme is used for privacypreserving data aggregation.…”

“…To solve these security problems, we introduce a double-masking scheme [39,40]. In the work [40], the double-masking scheme is used for privacypreserving data aggregation. And the scheme in [40] can also protect location privacy and verify the aggregation results.…”

“…In the work [40], the double-masking scheme is used for privacypreserving data aggregation. And the scheme in [40] can also protect location privacy and verify the aggregation results. In our model, location privacy protection is implemented by the interval judgment scheme, and our secure truth discovery will confirm the data consistency.…”