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On Learning Spatial Provenance in Privacy-Constrained Wireless Networks

Manish Bansal, Pramsu Srivastava, J. Harshan

TL;DR

The paper tackles the challenge of learning spatial provenance in privacy-constrained V2X multi-hop networks, where RSUs require vehicle location insights for security diagnostics but vehicles wish to protect their GPS privacy. It introduces a Correlated Linear Bloom Filters (CLBF) framework that embeds regional identities and packet-forwarding provenance into two Bloom filters, enabling RSUs to recover provenance without exposing exact GPS coordinates. The authors derive analytical expressions for false positive probabilities, formulate optimization problems to select Bloom filter parameters, and validate the approach with simulations that show near-optimal parameter choices and explicit trade-offs between communication overhead, privacy, and localization accuracy. This work advances privacy-preserving provenance for wireless vehicular networks and provides a practical design path for deploying location-aware security features under privacy constraints.

Abstract

In Vehicle-to-Everything networks that involve multi-hop communication, the Road Side Units (RSUs) typically aim to collect location information from the participating vehicles to provide security and network diagnostics features. While the vehicles commonly use the Global Positioning System (GPS) for navigation, they may refrain from sharing their precise GPS coordinates with the RSUs due to privacy concerns. Therefore, to jointly address the high localization requirements by the RSUs as well as the vehicles' privacy, we present a novel spatial-provenance framework wherein each vehicle uses Bloom filters to embed their partial location information when forwarding the packets. In this framework, the RSUs and the vehicles agree upon fragmenting the coverage area into several smaller regions so that the vehicles can embed the identity of their regions through Bloom filters. Given the probabilistic nature of Bloom filters, we derive an analytical expression on the error-rates in provenance recovery and then pose an optimization problem to choose the underlying parameters. With the help of extensive simulation results, we show that our method offers near-optimal Bloom filter parameters in learning spatial provenance. Some interesting trade-offs between the communication-overhead, spatial privacy of the vehicles and the error rates in provenance recovery are also discussed.

On Learning Spatial Provenance in Privacy-Constrained Wireless Networks

TL;DR

The paper tackles the challenge of learning spatial provenance in privacy-constrained V2X multi-hop networks, where RSUs require vehicle location insights for security diagnostics but vehicles wish to protect their GPS privacy. It introduces a Correlated Linear Bloom Filters (CLBF) framework that embeds regional identities and packet-forwarding provenance into two Bloom filters, enabling RSUs to recover provenance without exposing exact GPS coordinates. The authors derive analytical expressions for false positive probabilities, formulate optimization problems to select Bloom filter parameters, and validate the approach with simulations that show near-optimal parameter choices and explicit trade-offs between communication overhead, privacy, and localization accuracy. This work advances privacy-preserving provenance for wireless vehicular networks and provides a practical design path for deploying location-aware security features under privacy constraints.

Abstract

In Vehicle-to-Everything networks that involve multi-hop communication, the Road Side Units (RSUs) typically aim to collect location information from the participating vehicles to provide security and network diagnostics features. While the vehicles commonly use the Global Positioning System (GPS) for navigation, they may refrain from sharing their precise GPS coordinates with the RSUs due to privacy concerns. Therefore, to jointly address the high localization requirements by the RSUs as well as the vehicles' privacy, we present a novel spatial-provenance framework wherein each vehicle uses Bloom filters to embed their partial location information when forwarding the packets. In this framework, the RSUs and the vehicles agree upon fragmenting the coverage area into several smaller regions so that the vehicles can embed the identity of their regions through Bloom filters. Given the probabilistic nature of Bloom filters, we derive an analytical expression on the error-rates in provenance recovery and then pose an optimization problem to choose the underlying parameters. With the help of extensive simulation results, we show that our method offers near-optimal Bloom filter parameters in learning spatial provenance. Some interesting trade-offs between the communication-overhead, spatial privacy of the vehicles and the error rates in provenance recovery are also discussed.
Paper Structure (13 sections, 3 theorems, 6 equations, 5 figures)

This paper contains 13 sections, 3 theorems, 6 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Contributions
  3. Network model
  4. Routing Constraints
  5. Bloom Filter based Spatial-Provenance Recovery
  6. Embedding in Bloom filters
  7. Recovery from Bloom filter
  8. On the choice of the Bloom filter parameters
  9. Optimization of Bloom Filter Parameters
  10. On computing $\{C_{j}\}$
  11. On computing $\{f_{J}\}$
  12. Experimental Results
  13. Discussion

Key Result

Proposition 1

The probability of false positive event, denoted by, $\Pr(E_{fp})$ can be written using the Bayes' rule as: where $\Pr(E_{fp}|\alpha)$ denotes the false positive probability conditioned on $\alpha$ bits lit in $BF_{2}$, and $\Pr(\alpha)$ denotes the probability of $\alpha$ bits lit in the $BF_2$, which is given by suraj:

Figures (5)

  • Figure 1: Depiction of a network wherein the nodes are distributed over a geographical area, and the RSU intends to learn spatial-provenance of the nodes through data-logs.
  • Figure 2: Depiction of embedding in CLBF at the intermediate nodes which comprise edge and location Bloom filters of size $m_1 =8$ bits and $m_2=8$ bits, respectively. Here $k_1=k_2=3$ number of Hash functions are used for embedding an element in CLBF.
  • Figure 3: False positive probability using the analytical bound and the simulation results for varying values of $k_2$ on a network of $N = 16$, with $|\Delta| = 16$ and $|\Delta| =8$.
  • Figure 4: False positive probability using the analytical bound and the simulation results with varying $m_2$ on a network of $N = 11$ with $|\Delta| = 15$ and $h=10$.
  • Figure 5: False positive probability using the analytical bound and the simulation results with varying $|\Delta|$ and constant $m_2=100, ~h=15$.

Theorems & Definitions (11)

  • Definition 1
  • Proposition 1
  • Definition 2
  • Lemma 1
  • proof
  • Definition 3
  • Definition 4
  • Definition 5
  • proof
  • Proposition 2
  • ...and 1 more