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.

Users frequently struggle to decipher privacy policies, facing challenges due to the legalese often present in privacy policies, leaving trust and comprehension shrouded in ambiguity. This study dives into the transformative power of language, exploring how different linguistic tones can bridge the gap between legal, technical jargon, and genuine user engagement-through a comparative analysis involving diverse focus groups, immersing them in three distinct policy variations: legalistic, casual, and empathetic. We explored how these tones reshape the user experience and bridge the gap between legal discourse and comprehension. Analysis of the data revealed significant associations between linguistic tone and user trust and comprehension. The adoption of an empathetic tone significantly enhanced user trust, as evidenced by a 40.4% increase compared to alternative language styles. This preference highlights the human desire for genuine connection, even in the intricate domain of data privacy. Furthermore, comprehension indices arise for both empathetic and casual tones, leaving legalistic language lagging far behind. This suggests a clear path towards user-friendly policies, where clarity exceeds complexity. Our exploration goes beyond mere compliance. We illustrate the complex gap between subtle linguistic shifts and user perception. By deciphering the language that resonates with trust and understanding, We plant the seeds for the development of privacy policies that not only meet legal requirements but also enhance user trust and comprehension.