z-anonymity: Zero-Delay Anonymization for Data Streams
Nikhil Jha, Thomas Favale, Luca Vassio, Martino Trevisan, Marco Mellia

TL;DR
This paper introduces z-anonymity, a novel zero-delay anonymization method for data streams that is suitable for high-dimensional data and provides probabilistic guarantees of k-anonymity.
Contribution
It proposes z-anonymity, a new anonymization property for data streams that operates with zero delay and maps to k-anonymity probabilistically.
Findings
z-anonymity can be achieved with zero delay in data streams.
A probabilistic framework links z-anonymity to k-anonymity.
Real-world case study demonstrates practical effectiveness.
Abstract
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…
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