An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams

Abstract

A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.

References in corpus (5)

• Deep Learning with Differential Privacy
• Extremal Mechanisms for Local Differential Privacy
• Efficient privacy preservation of big data for accurate data mining
• Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining
• Benchmarking Distributed Stream Processing Platforms for IoT Applications

Cited by in corpus (6)

• Local Differential Privacy for Deep Learning
• Privacy Preserving Face Recognition Utilizing Differential Privacy
• Privacy Preserving Distributed Machine Learning with Federated Learning
• z-anonymity: Zero-Delay Anonymization for Data Streams
• EnclaveTree: Privacy-preserving Data Stream Training and Inference Using TEE
• Disentangling private classes through regularization