The task of infectious disease contact tracing is crucial yet challenging, especially when meeting strict privacy requirements. Previous attempts in this area have had limitations in terms of applicable scenarios and efficiency. Our paper proposes a highly scalable, practical contact tracing system called PREVENT that can work with a variety of location collection methods to gain a comprehensive overview of a person's trajectory while ensuring the privacy of individuals being tracked, without revealing their plain text locations to any party, including servers. Our system is very efficient and can provide real-time query services for large-scale datasets with millions of locations. This is made possible by a newly designed secret-sharing based architecture that is tightly integrated into unique private space partitioning trees. Notably, our experimental results on both real and synthetic datasets demonstrate that our system introduces negligible performance overhead compared to traditional contact tracing methods. PREVENT could be a game-changer in the fight against infectious diseases and set a new standard for privacy-preserving location tracking.

The widespread use of family tracing apps has highlighted the need for effective location privacy protection. Unfortunately, current solutions fail to provide stringent privacy protection or are computationally expensive, making them unsuitable for real-time services. In this paper, we propose a highly efficient system architecture that supports three common types of location-sharing queries (i.e., point queries, range queries, and k nearest neighbor queries) with strict privacy protection. The proposed design is based on the envisioned future collaborations between two social media platforms. One platform manages location privacy policies, while the other facilitates location collection and sharing requests. Our main contributions involve two new privacy-preserving query protocols. One is a highly efficient, generic secure comparison protocol for range queries. The other is a novel kNN query protocol that eliminates the need for computationally expensive secure sorting in existing solutions, thus offering unparalleled performance without compromising security. The paper provides a formal and rigorous security analysis of the proposed solutions using the Universally Composable framework. We also conduct extensive experiments that demonstrate that our approach is more than an order of magnitude faster than existing solutions.

Firewalls are effective in preventing attacks initiated from outside of an organization’s network, but they are vulnerable to external threats, e.g. ransomware attacks may expose sensitive firewall data to malicious entities or disable network protection from the firewall. In this paper, we present Obliv-FW: a novel distributed architecture and a suite of protocols to obliviously manage and evaluate firewall rules and policies to prevent external attacks oriented to the firewall data. Obliv-FW alleviates this issue by obfuscating the blacklist or whitelist and distributing the function of evaluating these lists across multiple servers residing in different access control zones of the organization’s internal network. Thus, both accessing and altering the rules are considerably more difficult thereby providing better protection to the local network as well as greater security for the firewall itself. Obliv-FW is developed by leveraging the existing secure multi-party computation techniques. Our empirical results show that the overhead of Obliv-FW is small, and it can be a very valuable tool to mitigate the ever-increasing threats to a private network from external attacks including ransomware attacks.