Privacy-preserving Navigation Supporting Similar Queries in Vehicular Networks

Li, Meng; Chen, Yifei; Zheng, Shuai; Hu, Donghui; Lal, Chhagan; Conti, Mauro

doi:10.1109/tdsc.2020.3017534

Cited by 26 publications

(29 citation statements)

References 35 publications

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“…Security threats come from internal and external adversaries [37], [38], [39]. Most raters are honest, and they follow the protocol by faithfully submitting ratings according to the received products or services [40], [41].…”

Section: B Security Modelmentioning

confidence: 99%

Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

Zhu

Zhang

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier's reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value.In this paper, we propose a blockchain-based privacypreserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all ecommerce platforms collaborate and share users' reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate oneshow anonymous credentials constructed from two-move blind signatures to protect customers' identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network.

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Section: B Security Modelmentioning

confidence: 99%

Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

Zhu

Zhang

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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“…PiSim [6] utilizes anonymous authentication [177] to authenticate users while not revealing their real identities. Each user registers to a trusted authority to obtain a group secret key, which is used to generate a temporary identity token for authentication.…”

Section: B Identity Privacymentioning

confidence: 99%

“…After a ride is over, the rider pays the SP with Ecash [180] issued on settled denominations at a random time. A rider and the matched driver exchange reputation tokens created by blind signatures [181] to anonymously rate each [4] VANET and fog-based secure and privacy-preserving navigation (SPNS) [5] Privacy-preserving navigation supporting similar queries (PiSim) [6] Ride-Hailing Privacy-preserving computation of meeting points in ridesharing (Priv-2SP-SP) [7] Differentially private scheduling for ridesharing (JDP-Ride) [8] Privacy-preserving and accountable ride-hailing (ORide) [9] Privacy-enhanced ride-hailing (PrivateRide) [109] Efficient and privacy-preserving dynamic spatial query for ride-hailing [143] Privacy-preserving ride-hailing matching with prediction (pRide) [144] Privacy-preserving group ridesharing matching (PGRide) [145] Efficient and privacy-preserving carpooling using fog computing and blockchain (FICA) [146] Privacy-preserving ride matching (pRide) [147] Lightweight and privacy-preserving ride matching (lpRide) [148] Privacy-preserving collaborative-ride hailing (CoRide) [149] Privacy-preserving ride-hailing with verifiable order-linking (OLink) [150] Privacy-preserving ride-hailing without a third trusted server [151] Smart Parking VANET-based smart parking (SPARK) [10] Privacy-preserving pay-by-phone parking [11] Anonymous smart-parking and payment (ASAP) [12] Privacy-preserving smart parking navigation (P-SPAN) [152] Secure automated valet parking for for autonomous vehicles [153] Privacy-preserving valet parking for autonomous driving (PrivAV) [154] Privacy-enhanced private parking spot sharing based on blockchain (PEPS)) [155] Distributed mobile system for free parking assignment (DFPS) [156] Privacy-preserving decentralized parking recommendation (PriParkRec) [157] Road Monitoring Distributed privacy-preserving traffic monitoring [13] Privacy-preserving vehicular crowdsensing based road surface condition monitoring (CLASC)…”

Section: B Identity Privacymentioning

confidence: 99%

“…Li et al [6] argue that repeatedly submitting the same locations to a navigation server will result in serious location privacy violation even if differential privacy is leveraged to perturb locations. In order to protect the drivers' location privacy, they transform querying a navigation route into querying traffic congestion.…”

Section: Location Privacymentioning

confidence: 99%

“…It has revolutionized how modern vehicles are manufactured, purchased, and operated. Typical vehicular services are navigation [4], [5], [6], ridehailing [7], [8], [9], smart parking [10], [11], [12], road monitoring [13], [14], [15], and Vehicular Digital Forensics (VDF) [16], [17], [18]. Navigation enables a requesting user to obtain an optimal route(s) from a pick-up location to a destination.…”

Section: Introductionmentioning

confidence: 99%

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Privacy for 5G-Supported Vehicular Networks

Zhu

Zhang

et al. 2021

IEEE Open J. Commun. Soc.

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Vehicular networks allow billions of vehicular users to be connected to report and exchange real-time data for offering various services, such as navigation, ride-hailing, smart parking, traffic monitoring, and vehicular digital forensics. Fifth generation (5G) is a new radio access technology with greater coverage, accessibility, and higher network density. 5G-supported Vehicular Networks (5GVNs) have attracted plenty of attention from both academia and industry. Geared with new features, they are expected to revolutionize the mobility ecosystem to empower a portfolio of new services. Meanwhile, the development of such communication capabilities, along with the development of sensory devices and the enhancement of local computing powers, have lead to an inevitable reality of massive data (e.g., identity, location, and trajectory) collection from vehicular users. Unfortunately, 5GVN are still confronted with a variety of privacy threats. Such threats are targeted at users' data, identity, location, and trajectory. If not properly handled, such threats will cause unimaginable consequences to users.In this survey, we first review the state-of-the-art of survey papers. Next, we introduce the architecture, features, and services of 5GVN, followed by the privacy objectives of 5GVN and privacy threats to 5GVN. Further, we present existing privacy-preserving solutions and analyze them in-depth. Finally, we define some future research directions to draw more attention and down-toearth efforts into this new architecture and its privacy issues.

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Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services

Chen

Zheng

et al. 2021

Lecture Notes in Computer Science

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Ride-Hailing Service (RHS) has motivated the rise of innovative transportation services. It enables riders to hail a cab or private vehicle at the roadside by sending a ride request to the Ride-Hailing Service Provider (RHSP). Such a request collects rider's real-time locations, which incur serious privacy concerns for riders. While there are many location privacy-preserving mechanisms in the literature, few of them consider mobility patterns or location semantics in RHS. In this work, we propose a pick-up location recommendation scheme with location indistinguishability and semantic indistinguishability for RHS. Specifically, we give formal definitions of location indistinguishability and semantic indistinguishability. We model the rider mobility as a time-dependent first-order Markov chain and generates a rider's mobility profile. Next, it calculates the geographic similarity between riders by using the Mallows distance and classifies them into different geographic groups. To comprehend the semantics of a location, it extracts such information through user-generated content from two popular social networks and obtains the semantic representations of locations. Cosine similarity and unified hypergraph are used to compute the semantic similarities between locations. Finally, it outputs a set of recommended pick-up locations. To evaluate the performance, we build our mobility model over the realworld dataset GeoLife, analyze the computational costs of a rider, show the utility, and implement it on an Android smartphone. The experimental results show that it costs less than 0.12 ms to recommend 10 pick-up locations within 500 m of walking distance.

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Privacy-preserving Navigation Supporting Similar Queries in Vehicular Networks

Cited by 26 publications

References 35 publications

Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

Privacy for 5G-Supported Vehicular Networks

Where to Meet a Driver Privately: Recommending Pick-Up Locations for Ride-Hailing Services

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