  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.
