Given the pervasiveness of Bluetooth Low Energy (BLE)-based devices, detecting unwanted or suspicious trackers is challenging, especially due to their heterogeneity, cross-platform compatibility issues, and inconsistent detection methods. BL(u)E CRAB …
Long queues in retail and public environments can frustrate customers and negatively impact user experiences. Traditional camera-based monitoring systems are effective in analyzing queues, however, the potential for identification raises privacy …
The constant connectedness of smart home devices and their sensing capabilities pose a unique threat to individuals' privacy. While users may expect devices to exhibit minimal activity while they are not performing their intended functions, this is …
Website privacy policies are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user-friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants …
GenAIPABench assesses the effectiveness of GenAIPAs across multiple dimensions including accuracy, relevance, and consistency, using a curated set of privacy-related questions and metrics. The benchmark aims to advance the development of AI privacy assistants by providing a standard evaluation framework.
In the age of data-driven technology, privacy has emerged as a critical concern for both users and organizations. Privacy policies are widely used to outline the data management practices of a company. However, it has been demonstrated that privacy …
Framework aimed at discovering, collecting, and analyzing privacy policies of smart devices using NLP and ML algorithms, to provide insights to users, policy authors, and regulators.
As the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, …
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations. However, traditional CF relies on a privacy-invasive collection of …