Biblio

The collection of digital information by governments, companies and individuals creates tremendous opportunities for knowledge and information-based decision-making. Driven by mutual benefit and laws and regulations, there is a need for data exchange and publication between all parties. However, data in its original form usually contains sensitive information about individuals and publishing such data would violate personal privacy. Privacy Protection Data Distribution (PPDP) provides methods and tools to release useful information while protecting data privacy. In recent years, PPDP has received extensive attention from the research community, and many solutions have been proposed for different data release scenarios. How to ensure the availability of data under the premise of protecting user privacy is the core problem to be solved in this field. This paper studies the existing achievements of privacy protection data release anonymity technology, focusing on the existing anonymity technology in three aspects of high-dimensional, high-deficiency, and complex relational data, and analyzes and summarizes them.

To improve dynamic updating of privacy protected data release caused by multidimensional sensitivity attribute privacy differences in relational data, we propose a dynamic updating method for privacy protection data release based on the multidimensional privacy differences. By adopting the multi-sensitive bucketization technology (MSB), this method performs quantitative classification of the multidimensional sensitive privacy difference and the recorded value, provides the basic updating operation unit, and thereby realizes dynamic updating of privacy protection data release based on the privacy difference among relational data. The experiment confirms that the method can secure the data updating efficiency while ensuring the quality of data release.

Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.

Preserving privacy is extremely important in data publishing. The existing privacy-preserving models are mostly oriented to single sensitive attribute, can not be applied to multiple sensitive attributes situation. Moreover, they do not consider the semantic similarity between sensitive attribute values, and may be vulnerable to similarity attack. In this paper, we propose a (l, m, d)-anonymity model for multiple sensitive attributes similarity attack, where m is the dimension of the sensitive attributes. This model uses the semantic hierarchical tree to analyze and compute the semantic dissimilarity between sensitive attribute values, and each equivalence class must exist at least l sensitive attribute values that satisfy d-different on each dimension sensitive attribute. Meanwhile, in order to make the published data highly available, our model adopts the distance-based measurement method to divide the equivalence class. We carry out extensive experiments to certify the (1, m, d)-anonymity model can significantly reduce the probability of sensitive information leakage and protect individual privacy more effectively.