An Efficient and Scalable Privacy Preserving Algorithm for Big Data and Data Streams
This addresses privacy concerns for data from sources like healthcare and smart systems, but it is incremental as it builds on existing perturbation techniques.
The paper tackles the challenge of preserving privacy in big data and data streams, where traditional methods often sacrifice data utility, by proposing the SEAL algorithm based on Chebyshev interpolation and Laplacian noise, which achieves a balance with high efficiency and scalability, showing empirical improvements in execution speed, accuracy, and attack resistance.
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.