# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 1 Introduction
However, the intersection of Tables 3 and 4 shown in Table 5 comprises only those individuals who have visited both hospitals or have the same _QI_ and sensitive values. Now, from Table 5, the adversary can link [PERSON]'s _QI_ values with the sensitive value, breaching [PERSON]'s personal privacy. Multiple independent data publishing poses new challenges for data privacy and the utility of the published data. In multiple independent data publications, a data owner does not know which published dataset may be used for a composition attack. Multiple independent data publications are different from traditional multiple-time data publications, such as multiple-view data publication [10; 11] and series data publication [12; 13; 14], in which a data publisher is familiar with all the datasets (different views or previous versions of the current dataset) that could be used for composition attacks and can use information in the known datasets to anonymize the current dataset. Since there is no communication or information sharing between data owners in multiple independent data publications, collaborative privacy-preserving data publishing techniques [19; 20; 21] cannot be used to protect privacy in this case. In addition, we published earlier research on privacy-preserving data publishing [15; 17; 22]. To reduce the likelihood of composition attacks on published datasets, existing anonymization techniques [23; 24; 2] utilize generalization and perturbation, which decrease the data utility. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Age** & **Sex** & **Zipcode** & **Disease** \\ \hline [PERSON] & 22 & M & 47905 & Gastritis \\ [PERSON] & 22 & F & 47905 & Ovarian Cancer \\ [PERSON] & 33 & F & 47901 & Breast Cancer \\ [PERSON] & 52 & M & 47901 & Flu \\ [PERSON] & 54 & M & 47902 & Dyspepsia \\ [PERSON] & 60 & F & 47902 & Fever \\ [PERSON] & 60 & F & 47308 & Cancer \\ [PERSON] & 64 & F & 47308 & Fever \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microdata of hospital A. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age** & **Sex** & **Zipcode** & **Disease** \\ \hline
22 & * & 47905 & Gastritis \\
22 & * & 47905 & Ovarian Cancer \\ \hline
33–52 & * & 47901 & Breast Cancer \\
33–52 & * & 47901 & Flu \\ \hline
54–60 & * & 47902 & Dyspepsia \\
54–60 & * & 47902 & Fever \\ \hline
60-64 & F & 47308 & Cancer \\
60-64 & F & 47308 & Fever \\ \hline \hline \end{tabular} \({}^{*}\) denotes the generalization of the attribute value.  
\end{table} 
Table 4: Anonymized table of hospital B (Mondrian). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Age** & **Sex** & **Zipcode** & **Disease** \\ \hline [PERSON] & 22 & M & 47905 & Gastritis \\ [PERSON] & 22 & M & 47905 & Blood Cancer \\ [PERSON] & 33 & F & 47907 & Breast Cancer \\ [PERSON] & 58 & M & 47903 & Flu \\ [PERSON] & 30 & M & 47902 & Dyspepsia \\ [PERSON] & 60 & F & 47902 & Fever \\ [PERSON] & 65 & F & 47308 & Flu \\ [PERSON] & 65 & F & 47309 & Cancer \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Microdata of hospital B. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age** & **Sex** & **Zipcode** & **Disease** \\ \hline
22 & * & 47905 & Gastritis \\
22 & * & 47905 & Ovarian Cancer \\ \hline
33–52 & * & 47901 & Breast Cancer \\
33–52 & * & 47901 & Flu \\ \hline
54–60 & * & 47902 & Dyspepsia \\
54–60 & * & 47902 & Fever \\ \hline
60-64 & F & 47308 & Cancer \\
60-64 & F & 47308 & Fever \\ \hline \hline \end{tabular} \({}^{*}\) denotes the generalization of the attribute value.  
\end{table} 
Table 3: Anonymized table of hospital A (Mondrian). In this paper, we use some ideas from [17] and propose a new approach called Merging for protection against composition attack in various independent data publications while preserving better data utility. It partitions the data both vertically and horizontally. In the vertical partitions, highly correlated attributes are grouped into columns and each resulting column will then contain a subset of attributes.