Reversible Data Perturbation Techniques for Multi-level Privacy-Preserving Data Publication

Abstract

Abstract. The amount of digital data generated in the Big Data age is increasingly rapidly. Privacy-preserving data publishing techniques based on differential privacy through data perturbation provide a safe release of datasets such that sensitive information present in the dataset cannot be inferred from the published data. Existing privacy-preserving data publishing solutions have focused on publishing a single snapshot of the data with the assumption that all users of the data share the same level of privilege and access the data with a fixed privacy level. Thus, such schemes do not directly support data release in cases when data users have different levels of access on the published data. While a straightforward approach of releasing a separate snapshot of the data for each possible data access level can allow multi-level access, it can result in a higher storage cost requiring separate storage space for each instance of the published data. In this paper, we develop a set of reversible data perturbation techniques for large bipartite association graphs that use perturbation keys to control the sequential generation of multiple snapshots of the data to offer multi-level access based on privacy levels. The proposed schemes enable multi-level data privacy, allowing selective deperturbation of the published data when suitable access credentials are provided. We evaluate the techniques through extensive experiments on a large real-world association graph dataset and our experiments show that the proposed techniques are efficient, scalable and effectively support multi-level data privacy on the published data.

Advanced extreme learning machine‐based ensemble classification scheme with enhanced data perturbation for human DNA sequences

“…Further, as medical records are associated with human subjects, privacy protection is considered as a serious issue when compared to other tasks related to mining 3 . Moreover, privacy preservation of medical datasets is essential for withholding the private details of patients and facilitating security of medical data 4 . In particular, the medical datasets that comprise of a large collection of patients' sensitive data need to be protected from any misuse 5 …”