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.
With the proliferation of digital transactions, trust is becoming increasingly important, as exemplified by the World Economic Forum’s Data Free Flow with Trust. Digital signatures are utilized to establish trust to prevent spoofing and unauthorized …
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 …
We present our experiences in adapting and deploying TIPPERS, a novel privacy-enabled IoT data collection and management system for smart spaces, to facilitate the monitoring of adherence to COVID-19 regulations in a university campus and a military …
This paper describes the collaborative effort between privacy and security researchers at nine different institutions along with researchers at the Naval Information Warfare Center to deploy, test, and demonstrate privacy-preserving technologies in …
Current approaches for enforcing Fine Grained Access Control (FGAC) in DBMS do not scale to scenarios when the number of access control policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart …
The success of emerging IoT applications depends on integrating privacy protections into the IoT infrastructures to guard against privacy risks posed by sensor-based continuous monitoring of individuals and their activities. This demonstration adapts …