Nyteisha Bookert, Mohd Anwar (North Carolina Agricultural and Technical State University)

Patient-generated health data is growing at an unparalleled rate due to advancing technologies (e.g., the Internet of Medical Things, 5G, artificial intelligence) and increased consumer transactions. The influx of data has offered life-altering solutions. Consequently, the growth has created significant privacy challenges. A central theme to mitigating risks is promoting transparency and notifying stakeholders of data practices through privacy policies. However, natural language privacy policies have several limitations, such as being difficult to understand (by the user), lengthy, and having conflicting requirements. Yet they remain the de facto standard to inform users of privacy practices and how organizations follow privacy regulations. We developed an automated process to evaluate the appropriateness of combining machine learning and custom named entity recognition techniques to extract IoMT-relevant privacy factors in the privacy policies of IoMT devices. We employed machine learning and the natural language processing technique of named entity recognition to automatically analyze a corpus of policies and specifications to extract privacy-related information for the IoMT device. Based on the natural language analysis of policies, we provide fine-grained annotations that can help reduce the manual and tedious process of policy analysis and aid privacy engineers and policy makers in developing suitable privacy policies.

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Privacy-Preserving Database Fingerprinting

Tianxi Ji (Texas Tech University), Erman Ayday (Case Western Reserve University), Emre Yilmaz (University of Houston-Downtown), Ming Li (CSE Department The University of Texas at Arlington), Pan Li (Case Western Reserve University)

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A Field Study to Uncover and a Tool to...

Leon Kersten (Eindhoven University of Technology), Kim Beelen (Eindhoven University of Technology), Emmanuele Zambon (Eindhoven University of Technology), Chris Snijders (Eindhoven University of Technology), Luca Allodi (Eindhoven University of Technology)

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StealthyIMU: Stealing Permission-protected Private Information From Smartphone Voice Assistant...

Ke Sun (University of California San Diego), Chunyu Xia (University of California San Diego), Songlin Xu (University of California San Diego), Xinyu Zhang (University of California San Diego)

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Case Study – Exploring Children’s Password Knowledge and Practices

Yee-Yin Choong, Mary Theofanos (NIST); Karen Renaud, Suzanne Prior (Abertay University)

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