As urbanization and the use of public transportation increases, it’s essential that city planners understand the movements of travellers. But protecting travellers’ privacy is also important.
The research
In our research, we looked at the difficulties of using wireless network access points or smart card check-ins for the counting and monitoring and monitoring of travelers in different locations. The main issue is how to manage uniquely identifying data while still adhering to strict privacy regulations.
Our paper proposes a novel solution based on encrypted Bloom filters to address this privacy issue. Bloom filters, which are probabilistic data structures, are used to represent sets while providing privacy under strong cryptographic assurances. In this system, encrypted Bloom filters give statistical counts of travelers as the only available information, guaranteeing a high level of privacy protection
Where is the data collected?
The proposed system model revolves around a subway network with an automated fare collection system. Sensors located at the stations collect encrypted data and transmit it to a server without decrypting it. This period of time during which detections are collected and combined is referred to as an "epoch," usually lasting five minutes. The server can process the encrypted collections of detections but cannot reconstruct individual detections. Homomorphic encryption is used to enable mathematical operations on encrypted data, providing an extra layer of security.
How do we test the privacy protections?
To assess the effectiveness of the privacy-preserving approach, we use a simulated subway dataset. The accuracy of the approach is compared to the actual data, and the research discusses the differences based on the counting technique.
Two counting techniques are presented, emphasizing the importance of proper implementation for precise results.
1. Union of Intersections:
• Calculates the size of the union of intersections, providing an estimation of the number of travelers that is closer to the truth.
2. Intersection Before Union:
• Calculates the size of the intersection between each departure epoch at station A and each arrival epoch at station B, which can lead to potential overestimation due to false positives.
The results
The proposed privacy-preserving method was found to be highly effective, as it was able to accurately count travelers while protecting their individual privacy. We highlighted the potential of the method to handle more intricate queries involving multiple locations, suggesting its usefulness in practical scenarios.
In conclusion, our paper highlighted the need to strike a balance between accurate traveler counting and stringent privacy protection. The proposed method, while introducing some loss of accuracy, provides a viable solution for protecting personal data in public transportation systems.
Future research
Future research should focus on the practicality of queries, the accuracy of different query scenarios, and the applicability of the method to more complex network structures. This privacy-preserving approach has the potential to improve public transportation efficiency while respecting individual privacy rights.
To find out more about this study, you can read here the original journal article, How to count travelers without tracking them between locations.
