Abstract
The Internet of Things (IoT) has revolutionized various domains through real-time decision-making and automation, with sensors serving as foundational components. Zero-permission sensors, such as accelerometers and gyroscopes, are widely used in most IoT systems, enabling unrestricted data access without permission. Despite being traditionally perceived as low-sensitivity, recent studies have shown that these sensors can be exploited to infer sensitive personal information, raising serious privacy concerns. Local Differential Privacy (LDP) is a rigorous technique for numerical data privacy protection even when the data collector, such as an IoT service provider, is untrusted. LDP ensures privacy by adding noise to sensor readings before transmission, limiting potential data leakage. The amount of noise is controlled by a privacy budget epsilon, which defines the trade-off between privacy protection and data utility - a smaller epsilon means stronger privacy but lower data accuracy. Existing LDP-based solutions distribute the privacy budget uniformly across sensors, making each sensor's budget inversely proportional to the number of sensors. This approach introduces excessive noise, especially with many sensors, significantly degrading IoT service quality.
Library of Congress Subject Headings
Internet of things--Security measures; Data privacy; Gyroscopes--Security measures; Accelerometers--Security measures
Publication Date
12-13-2024
Document Type
Thesis
Student Type
Graduate
Degree Name
Cybersecurity (MS)
Department, Program, or Center
Cybersecurity, Department of
College
Golisano College of Computing and Information Sciences
Advisor
Yidan Hu
Advisor/Committee Member
Sumita Mishra
Advisor/Committee Member
Hanif Rahbari
Recommended Citation
Liu, Xinyi, "Data Privacy Protection for Zero-Permission Sensors in IoT Systems" (2024). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11968
Campus
RIT – Main Campus
Plan Codes
COMPSEC-MS
