Revisiting Driver Anonymity in ORide

Kumaraswamy, Deepak; Murthy, Shyam; Vivek, S. Sree

doi:10.1007/978-3-030-99277-4_2

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…Note that LBS knows (𝑥 1 , 𝑦 1 ), i.e., as plaintexts. If (𝑋 𝑣𝑎 , 𝑌 𝑣𝑎 ) are encoded as (scaled) integers, then it will only have a "couple" of solutions on an average [8]. But to keep things simple, we can write similar equations for 𝑑 2 , 𝑑 3 , ....…”

Section: Security Implicationsmentioning

confidence: 99%

Revisiting a Privacy-Preserving Location-based Service Protocol using Edge Computing

Upadhyaya

Vivek

2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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Location-based services are getting more popular day by day. Finding nearby stores, proximity-based marketing, on-road service assistance, etc., are some of the services that use location-based services. In location-based services, user information like user identity, user query, and location must be protected. Ma et al. (INFOCOM-BigSecurity 2019) proposed a privacy-preserving location-based service using Somewhat Homomorphic Encryption (SHE). Their protocol uses edge nodes that compute on SHE encrypted location data and determines the 𝑘-nearest points of interest contained in the Location-based Server (LBS) without revealing the original user coordinates to LBS, hence, ensuring privacy of users locations.In this work, we show that the above protocol by Ma et al. has a critical flaw. In particular, we show that their secure comparison protocol has a correctness issue in that it will not lead to correct comparison. A major consequence of this flaw is that straightforward approaches to fix this issue will make their protocol insecure. Namely, the LBS will be able to recover the actual locations of the users in each and every query. CCS CONCEPTS• Security and privacy → Privacy-preserving protocols.

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Section: Security Implicationsmentioning

confidence: 99%

Revisiting a Privacy-Preserving Location-based Service Protocol using Edge Computing

Upadhyaya

Vivek

2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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“…They use homomorphic encryption to compute the Euclidean distance and identify the closest driver in a zone. [5] proposes a modification to ORide to ensure location privacy of responding drivers in the region with respect to a rider.…”

Section: Algorithm 1: Rv Recovers Quadtreementioning

confidence: 99%

Cryptanalysis of the Privacy-Preserving Ride-Hailing Service TRACE

Kumaraswamy,

Vivek

2021

Preprint

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In a typical ride-hailing service, the service provider (RS) matches a customer (RC) with the closest vehicle (RV) registered to this service. Ride-hailing services have gained tremendous popularity over the past years, and several works have been proposed to ensure privacy of riders and drivers during ride-matching. TRACE is an efficient privacypreserving ride-hailing service proposed by Wang et al. (IEEE Trans. Vehicular Technology 2018). TRACE uses masking along with other cryptographic techniques to ensure efficient and accurate ride-matching. RS computes a (secret) spatial division of a region into quadrants. The RS uses masked location information to match RCs and RVs within a quadrant without obtaining their exact locations, thus ensuring privacy. Additionally, an RC only gets to know location of the closest RV finally matched to it, and not of other responding RVs in the region. In this work, we disprove the privacy claims in TRACE by showing the following: a) RCs and RVs can identify the secret spatial division maintained by RS (this reveals information about the density of RVs in the region and other potential trade secrets), and b) the RS can identify exact locations of RCs and RVs (this violates location privacy). Prior to exchanging encrypted messages in the TRACE protocol, each entity masks the plaintext message with a secret unknown to others. Our attack allows other entities to recover this plaintext from the masked value by exploiting shared randomness used across different messages, that eventually leads to a system of linear equations in the unknown plaintexts. This holds even when all the participating entities are honest-but-curious. We implement our attack and demonstrate its efficiency and high success rate. For the security parameters recommended for TRACE, an RV can recover the spatial division in less than a minute, and the RS can recover the location of an RV in less than a second on a commodity laptop.

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Passive Triangulation Attack on ORide

Murthy¹,

Vivek²

2022

Cryptology and Network Security

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Revisiting Driver Anonymity in ORide

Cited by 8 publications

References 21 publications

Revisiting a Privacy-Preserving Location-based Service Protocol using Edge Computing

Revisiting a Privacy-Preserving Location-based Service Protocol using Edge Computing

Cryptanalysis of the Privacy-Preserving Ride-Hailing Service TRACE

Passive Triangulation Attack on ORide

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