An Anonymous Data Publishing Framework for Streaming Genomic Data

摘要

The sharing of streaming genomic data, which are mainly DNA sequences, necessitates a high degree of protection against potential privacy breaches through the application of data anonymization. Although the currently available hybrid algorithm is considered to be successful for anonymously publishing incremental DNA sequences that come in streams, it is limited in its utility since it can easily overgeneralize the genomic data by generating too many clusters with three sequences. In this paper, we proposes a high-accuracy framework with delayed constraint for preserving the privacy of streaming genomic data. The framework has two possible algorithms: one with suppression and the other without. Unlike the hybrid algorithm, our algorithms generate fewer clusters with three sequences, leading to an increase in utility of the anonymization results. The experimental results demonstrate that the presented framework is more effective than existing algorithms in anonymizing streaming genomic data. Moreover, the high utility of the anonymous data is more beneficial for researchers working toward data mining.