[Paper Review] PPLS: A Privacy-Preserving Location-Sharing Scheme in Vehicular Social Networks

Chang Xu, Xuan Xie+5 more|arXiv (Cornell University)|Apr 6, 2018

Privacy-Preserving Technologies in Data26 references3 citations

TL;DR

This paper proposes PPLS, a privacy-preserving location-sharing scheme for vehicular social networks that enables vehicles to set different threshold distances for different friends while protecting the privacy of these distances. It introduces a secure distance comparison protocol to compare encrypted distances without revealing sensitive values, ensuring location and social relationship privacy, with security analysis and performance evaluation confirming its efficiency and robustness.

ABSTRACT

Recent advances in Socially Aware Networks (SANs) have allowed its use in many domains, out of which social Internet of vehicles (SIOV) is of prime importance. SANs can provide a promising routing and forwarding paradigm for SIOV by using interest-based communication. Though able to improve the forwarding performance, existing interest-based schemes fail to consider the important issue of protecting users' interest information. In this paper, we propose a PRivacy-preserving Interest-based Forwarding scheme (PRIF) for SIOV, which not only protects the interest information, but also improves the forwarding performance. We propose a privacy-preserving authentication protocol to recognize communities among mobile nodes. During data routing and forwarding, a node can know others' interests only if they are affiliated with the same community. Moreover, to improve forwarding performance, a new metric {\em community energy} is introduced to indicate vehicular social proximity. Community energy is generated when two nodes encounter one another and information is shared among them. PRIF considers this energy metric to select forwarders towards the destination node or the destination community. Security analysis indicates PRIF can protect nodes' interest information. In addition, extensive simulations have been conducted to demonstrate that PRIF outperforms the existing algorithms including the BEEINFO, Epidemic, and PRoPHET.

Motivation & Objective

• To address the lack of privacy protection for threshold distances in existing vehicular social network (VSN) location-sharing schemes.
• To enable vehicles to define different threshold distances for different friends, supporting flexible, personalized access control policies.
• To prevent adversaries from inferring vehicle identities, social relationships, or location data through analysis of threshold distance patterns.
• To design a secure distance comparison protocol that allows encrypted distance comparison without revealing actual or threshold distances to untrusted servers.
• To develop a complete, efficient, and secure location-sharing scheme (PPLS) that avoids broadcast encryption and supports diverse query types.

Proposed method

• Designing a secure distance comparison protocol based on homomorphic encryption to compare encrypted actual and threshold distances without revealing either value.
• Integrating the secure distance comparison protocol into a novel location-sharing framework that supports per-friend threshold distances.
• Using asymmetric encryption and key management techniques to ensure that neither the location server nor social network server learns complete user profiles or location data.
• Replacing broadcast encryption with targeted queries to reduce overhead and prevent leakage of social network topology.
• Employing dummy queries and private set intersection techniques to further obscure user identities and query patterns.
• Defining a new query model that allows vehicles to retrieve location information based on encrypted distance comparisons and friend-specific thresholds.

Experimental results

Research questions

• RQ1 How can vehicles in VSNs set different threshold distances for different friends while preserving the privacy of these distances?
• RQ2 What cryptographic protocol can securely compare encrypted distances without exposing the actual values to untrusted servers?
• RQ3 How can a location-sharing scheme be designed to avoid broadcast encryption while maintaining fine-grained access control?
• RQ4 What is the performance overhead of the proposed secure distance comparison protocol in a real-world vehicular environment?
• RQ5 To what extent does the scheme resist privacy attacks based on threshold distance correlation and pattern analysis?

Key findings

• The proposed secure distance comparison protocol enables encrypted comparison of actual and threshold distances without revealing either value, effectively protecting sensitive distance data.
• PPLS supports per-friend threshold distances, allowing vehicles to define flexible, personalized access control policies based on social proximity.
• The scheme prevents location servers from learning users' social network topologies, as broadcast encryption is replaced with targeted queries.
• Security analysis confirms that PPLS resists common attacks, including insider threats and re-identification via threshold distance patterns.
• Performance evaluation shows that the protocol’s time consumption is dominated by distance comparison and decryption steps, but remains efficient for real-time vehicular applications.
• The scheme outperforms prior works like MobiShare, N-MobiShare, and MobiShare+ in both privacy protection and operational efficiency.