IoT-Privacy: To be private or not to be private

Abstract

Privacy breaching attacks pose considerable challenges in the development and deployment of Internet of Things (IoT) applications. Though privacy preserving data mining (PPDM) minimizes sensitive data disclosure probability, sensitive content analysis, privacy measurement and user's privacy awareness issues are yet to be addressed. In this paper, we propose a privacy management scheme that enables the user to estimate the risk of sharing private data like smart meter data. Our focus is to develop robust sensitivity detection, analysis and privacy content quantification scheme from statistical disclosure control aspect and information theoretic model. We depict performance results using real sensor data.

On perspective of security and privacy-preserving solutions in the internet of things

“…They argue that privacy aspects should be considered very seriously because the IoT applications often work with sensitive data and personal data. There are also papers that focus mainly on privacy in IoT, such as [24], [25]. These papers analyze possible threats and challenges.…”

Cardiac Condition Monitoring through Photoplethysmogram Signal Denoising Using Wearables: Can We Detect Coronary Artery Disease with Higher Performance Efficacy?

“…Outlier detection is an interesting research method applied to various domains [14]. Given a series of Ω , , the challenge we face is to distinctly differentiate noisy and clean segments.…”

“…However, different aspects like security, privacy, quality of service guarantees while capturing and processing the sensitive data like health parameters are to be addressed [5,9,13,14] along with building precise sensors and analytics solution.…”

SPA: smart meter privacy analyzer

“…Firstly, we derive distribution pattern of φ κ through sample excess kurtosis (κ) measurement. For κ > 3, (leptokurtic distribution), modified Rosner filtering is executed to minimize swamping effect [3], [2]. Instead of sample mean, we use sample median for Rosner backward selection method.…”

“…how much the probability of finding Ω, given φ, i.e. the information leakage transfer function = privacy quantification value = ρ= [3]. We normalize ρ to privacy score [1,10].…”