Faanst: Fast Anonymizing Algorithm For Numerical Streaming Data

Streaming data is widely used in today's world. Data comes from different sources in streams, and must be processed online and with minimum delay. These data streams usually contain confidential data such as customers' purchase information, and need to be mined in order to reveal other useful information like customers' purchase patterns. Privacy preservation throughout these processes plays a crucial role. K-anonymity is a well-known technique for preserving privacy. The principle issues in k-anonymity are data loss and running time. Although some of the existing k-anonymity techniques are able to generate anonymized data with acceptable data loss, their main drawback is that they are very time consuming, and are not applicable in a streaming context since streaming data is usually very sensitive to delay, and needs to be processed quite fast. In this paper, we propose a cluster-based k-anonymity algorithm called FAANST (Fast Anonymizing Algorithm for Numerical Streaming daTa) which can anonymize numerical streaming data quite fast, while providing an admissible data loss. We also show that FAANST can be easily extended to support data streams consisting of categorical values as well as numerical values.