Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining

Abstract

The widespread use of the Internet of Things (IoT) has raised many concerns, including the protection of private information. Existing privacy preservation methods cannot provide a good balance between data utility and privacy, and also have problems with efficiency and scalability. This paper proposes an efficient data stream perturbation method (named as $P^{2}RoCAl$). $P^{2}RoCAl$ offers better data utility than similar methods: classification accuracies of $P^{2}RoCAl$ perturbed data streams are very close to those of the original data streams. $P^{2}RoCAl$ also provides higher resilience against data reconstruction attacks.

arXiv commentPervasive and Mobile Computing 2018

References in corpus (4)

• Extremal Mechanisms for Local Differential Privacy
• Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
• Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
• Benchmarking Distributed Stream Processing Platforms for IoT Applications

Cited by in corpus (9)

• Local Differential Privacy for Deep Learning
• Privacy Preserving Face Recognition Utilizing Differential Privacy
• Privacy Preserving Distributed Machine Learning with Federated Learning
• Efficient privacy preservation of big data for accurate data mining
• An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams
• A Survey of Privacy Vulnerabilities of Mobile Device Sensors
• A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
• PPaaS: Privacy Preservation as a Service
• HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation