6 research outputs found
z-anonymity: Zero-Delay Anonymization for Data Streams
With the advent of big data and the birth of the data markets that sell
personal information, individuals' privacy is of utmost importance. The
classical response is anonymization, i.e., sanitizing the information that can
directly or indirectly allow users' re-identification. The most popular
solution in the literature is the k-anonymity. However, it is hard to achieve
k-anonymity on a continuous stream of data, as well as when the number of
dimensions becomes high.In this paper, we propose a novel anonymization
property called z-anonymity. Differently from k-anonymity, it can be achieved
with zero-delay on data streams and it is well suited for high dimensional
data. The idea at the base of z-anonymity is to release an attribute (an atomic
information) about a user only if at least z - 1 other users have presented the
same attribute in a past time window. z-anonymity is weaker than k-anonymity
since it does not work on the combinations of attributes, but treats them
individually. In this paper, we present a probabilistic framework to map the
z-anonymity into the k-anonymity property. Our results show that a proper
choice of the z-anonymity parameters allows the data curator to likely obtain a
k-anonymized dataset, with a precisely measurable probability. We also evaluate
a real use case, in which we consider the website visits of a population of
users and show that z-anonymity can work in practice for obtaining the
k-anonymity too
An efficient and scalable privacy preserving algorithm for big data and data streams
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