Privacy-preserving Data Publishing Techniques Research Articles

EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing

The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of a person’s private information represents a serious threat to his/her privacy. Privacy-Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challenging process. This study proposes the Efficient Data Anonymization Model Selector (EDAMS) for PPDP which generates an optimized anonymized dataset in terms of privacy and utility. EDAMS inputs the dataset with required parameters and produces its anonymized version by incorporating PPDP techniques while balancing utility and privacy. EDAMS is currently incorporating three PPDP techniques, namely k-anonymity, l-diversity, and t-closeness. It is tested against different variations of three datasets. The results are validated by testing each variation explicitly with the stated techniques. The results show the effectiveness of EDAMS by selecting the optimum model with minimal effort.

Privacy-aware data publishing against sparse estimation attack

Recently a number of privacy-preserving data publishing techniques have been proposed to protect the privacy of released data. In this paper, we study how to protect unreleased data privacy during data publishing. Our experiment results show that the unreleased data could be well estimated when an attacker leverages sparse estimation techniques, even when a large amount of noise is randomly added to the released data. To address unreleased data privacy while guaranteeing the utility of released data, we propose a privacy-aware structural data publishing framework against sparse estimation attack. Specifically, we present a nonzero element Gaussian random noise addition strategy, which is realized by maximizing global information loss between original data and noisy data. Furthermore, we deduce the upper bound of the number of released data that could be published, which acts as the criterion to guarantee the unreleased data privacy. Our experiment results show that the proposed framework is able to protect unreleased data privacy with desirable performance.

Privacy preserving big data publishing: a scalable k ‐anonymization approach using MapReduce

Big data is collected and processed using different sources and tools that lead to privacy issues. Privacy preserving data publishing techniques such as k -anonymity, l -diversity, and t -closeness are used to de-identify the data; however, the chances of re-identification are always remain present since data is collected from multiple sources. Owing to the large volume of data, less generalisation or suppression is required to achieve the same level of privacy, which is also known as `large crowd effect', although it is always challenging to handle such a large data for anonymization. MapReduce handles large volume of data and distributes the data into the smaller chunks across the multiple nodes; consequently, the full advantage of large volume of data is underachieved. Therefore, scalability of privacy preserving techniques becomes a challenging area of research. The authors explore this area and propose an algorithm named scalable k -anonymization (SKA) using MapReduce for privacy preserving big data publishing. The authors also compare the approach with existing approaches that results into a remarkable improvement of the data utility and significantly enhances the performance in terms of running time.

Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing

Current privacy preserving data publishing techniques concentrate on tables with only one sensitive attribute. However, most of the real-world applications contain multiple sensitive attributes. Directly applying the existing single-sensitive-attribute privacy preserving techniques often causes unexpected private information disclosure. This paper firstly discusses the problem of secure publishing data when sensitive data contains multi attributes, and then propose a multi-dimensional bucket grouping approach on the idea of lossy join, called Multi-Sensitive Bucketization (MSB). In order to avoid exhausting search, three specific line-time greedy based MSB algorithms are proposed, which are maximal-bucket first algorithm (MBF), maximal single-dimension-capacity first algorithm (MSDCF), and maximal multi-dimension-capacity first algorithm (MMDCF). In addition, according to the differences among published data, a weighted MSB approach is further proposed. Experimental results on the real-world datasets show that the addition information loss of the proposed MSB methods were not more than 0.04 and the suppression ratios were less than 0.06. The weighted MSB approach can guarantee more than 70% publishing ratio.