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Cryptanalysis of a Privacy-Preserving Ride-Hailing Service from NSS 2022

Srinivas Vivek

TL;DR

The paper critiques a privacy-preserving ride-hailing protocol (XieCGLJ22) by presenting a passive attack wherein an honest-but-curious Service Provider reconstructs the exact rider location $E(u_P)$ and the responding drivers' locations $E(u_{T_k})$ from encrypted blocks. The attack exploits per-block signed differences observed across multiple drivers within the same zone and time slot, using a lemma that allows recovering $[E_i(u_P)]_j$ from the set of offsets $([E_i(u_{T_k})]_j-[E_i(u_P)]_j)$. Notably, the attack is efficient and independent of the cryptographic security parameter, undermining the protocol’s claimed privacy guarantees even in the presence of mitigations such as limited data exposure and constrained matching. These findings question the robustness of the security claims in XieCGLJ22 and emphasize the need for stronger cryptographic protections to prevent leakage of location information in PP-RHS systems.

Abstract

Ride-Hailing Services (RHS) match a ride request initiated by a rider with a suitable driver responding to the ride request. A Privacy-Preserving RHS (PP-RHS) aims to facilitate ride matching while ensuring the privacy of riders' and drivers' location data w.r.t. the Service Provider (SP). At NSS 2022, Xie et al. proposed a PP-RHS. In this work, we demonstrate a passive attack on their PP-RHS protocol. Our attack allows the SP to completely recover the locations of the rider as well as that of the responding drivers in every ride request. Further, our attack is very efficient as it is independent of the security parameter.

Cryptanalysis of a Privacy-Preserving Ride-Hailing Service from NSS 2022

TL;DR

The paper critiques a privacy-preserving ride-hailing protocol (XieCGLJ22) by presenting a passive attack wherein an honest-but-curious Service Provider reconstructs the exact rider location and the responding drivers' locations from encrypted blocks. The attack exploits per-block signed differences observed across multiple drivers within the same zone and time slot, using a lemma that allows recovering from the set of offsets . Notably, the attack is efficient and independent of the cryptographic security parameter, undermining the protocol’s claimed privacy guarantees even in the presence of mitigations such as limited data exposure and constrained matching. These findings question the robustness of the security claims in XieCGLJ22 and emphasize the need for stronger cryptographic protections to prevent leakage of location information in PP-RHS systems.

Abstract

Ride-Hailing Services (RHS) match a ride request initiated by a rider with a suitable driver responding to the ride request. A Privacy-Preserving RHS (PP-RHS) aims to facilitate ride matching while ensuring the privacy of riders' and drivers' location data w.r.t. the Service Provider (SP). At NSS 2022, Xie et al. proposed a PP-RHS. In this work, we demonstrate a passive attack on their PP-RHS protocol. Our attack allows the SP to completely recover the locations of the rider as well as that of the responding drivers in every ride request. Further, our attack is very efficient as it is independent of the security parameter.
Paper Structure (4 sections, 1 theorem, 16 equations, 1 table)

This paper contains 4 sections, 1 theorem, 16 equations, 1 table.

Table of Contents

  1. Introduction
  2. PP-RHS Protocol from XieCGLJ22
  3. Our Attack
  4. Conclusion

Key Result

lemma 1

Let $x \in \{0,1,2,\ldots,2^l-1\}$. Given all the (signed) differences $\{z-x\;|\; 0 \le z \le 2^l-1 \}$ (in any order), then we can uniquely recover $x$.

Theorems & Definitions (1)

  • lemma 1