# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 2 Background and Related Work
### Related Work  
Numerous privacy-preserving data publishing methods have been reported in the last few decades, based on partitioning and randomization. In partitioning methods, the data values of quasi-identifiers (e.g., age, sex and zipcode) are generalized to create an equivalence class, so that individuals cannot be identified with their sensitive values in the equivalence class. By contrast, in randomization methods, the original values are changed by adding noise to make it difficult to pinpoint an individual in a published dataset. Some popular anonymization techniques such as \(k\)-anonymity [6], \(l\)-diversity [7], \(t\)-closeness [8], JS-reduce [30] have been developed for privacy preservation in one-time data publishing. Among these methods, JS-reduce [30] is the only method which models sequential background knowledge attack. The proposed method provides a better model which considers sequential background knowledge attack and anonymizes data, which gives better privacy protection to the individual. For the multiple sensitive values in the dataset, a multidimensional framework was proposed for the privacy-preserving data publishing [31]. The multidimensional framework partitions the \(QI\) attributes and sensitive attributes in the bucket and ensures the privacy by satisfying \(l\)-diversity and \(k\)-anonymity privacy requirements. For the multiple numerical sensitive values, the multi sensitive bucketiazation method was proposed based on cluster technology for privacy-preserving data publishing [32]. In addition, several recent approaches [33; 34; 35] have been proposed to anonymize and publish a dataset while preserving more data utility. However, these methods are vulnerable to composition attack. Several privacy-preserving data publishing methods have been designed to take into account known releases of related datasets, such as earlier publications by the same data owner (called sequential, serial or incremental releases) [13; 14] and multiple views of the same dataset [11]. These methods explicitly deal with composition attacks which, by applying the intersection of two or more published datasets, reveal the sensitive information of any individual. Generalization [36] is a popular method that has been widely used for data anonymization. While generalization may protect personal data privacy, it results in serious data utility issues during data publication. In the last decade, _Hybrid_[2], _Probabilistic_[23] and _Composition_[24] privacy methods have been proposed for multiple independent data publishing. Composition is the first privacy model to preventagainst composition attacks in the multiple independent data publishing context. The proposed method in [23] uses sampling and generalization for independent datasets to protect composition attacks. The probabilistic approach tries to establish the linkability of sensitive values shared between multiple independent datasets by exploring correlation the \(QI\) attributes and sensitive attributes to simulate the anonymized data from another organization. The method in [24] works on the top of _k-anonymization_[6] privacy model to protect datasets from composition attacks. Hybrid method combines random sampling, perturbation, and generalization to protect the independent datasets from composition attacks. Hybrid provides better privacy protection and data utility than the Composition and Probabilistic methods. Recently, \(\epsilon-differential\ privacy\left(\epsilon-DP\right)\)[37] has received substantial attention for privacy-preserving data publishing. In differential privacy, \(\epsilon-DP\) provides a strong privacy guarantee for statistical query answering. A composition attack can be protected by differential privacy based data anonymization [18]. A survey on differential privacy can be found in [38]. An anonymization mechanism on a dataset satisfies \(\epsilon-DP\) privacy if the deletion or insertion of a single record from the dataset has only a small effect on the output of the randomization technique.