Set-valued Data Anonymization Maintaining Data Utility and Data Property

Abstract

Set-valued database publication has been attracting much attention due to its benefit for various applications like recommendation systems and marketing analysis. However, publishing original database directly is risky since an unauthorized party may violate individual privacy by associating and analyzing relations between individuals and set of items in the published database, which is known as identity linkage attack. Generally, an attack is performed based on attacker's background knowledge obtained by a prior investigation and such adversary knowledge should be taken into account in the data anonymization. Various data anonymization schemes have been proposed to prevent the identity linkage attack. However, in existing data anonymization schemes, either data utility or data property is reduced a lot after excessive database modification and consequently data recipients become to distrust the released database. In this paper, we propose a new data anonymization scheme, called sibling suppression, which causes minimum data utility lost and maintains data properties like database size and the number of records. The scheme uses multiple sets of adversary knowledge and items in a category of adversary knowledge are replaced by other items in the category. Several experiments with real dataset show that our method can preserve data utility with minimum lost and maintain data property as the same as original database. CCS CONCEPTS • Security and privacy → Data anonymization and sanitization; Database and storage security;

Proximity Measurement for Hierarchical Categorical Attributes in Big Data

“…When anonymizing a data set, we do not have to focus only on ensuring privacy in big data but also on preserving data utility before publishing the anonymized data set. ere are various metrics to quantify the usefulness of the data [15,16]. In this paper, we will focus on the NCP in order to evaluate the results of our proposed algorithm.…”

“…In the literature, several studies have used the NCP to measure the information loss [14][15][16][17][18]. e utility is mostly…”

“…us, the adversary will be prevented from knowing, for example, the exact type of a patient's disease. Our proposed algorithm will be evaluated based on an effective measurement, which is NCP in order to measure the degree of information loss [14][15][16][17][18].…”

Swapping-based Data Sanitization Method for Hiding Sensitive Frequent Itemset in Transaction Database

“…However, this action may distort item correlation in the transactions that result in significant changes in a sanitized database content [28]. To address this problem, in this research, the strategy in [5] is adopted. The key point of the strategy is checking whether items i ∈ t x that will be swapped are coexisting with that in t ′…”

“…The proposed method follows the swappingbased strategy to ensure privacy protection in a database while at the same time preventing excessive side effects of the data sanitization process. The method follows a recent data swapping method that has been developed in [5] to generate an anonymized database. The proposed method uses an item collision detection strategy, and it carefully selects a pair of transaction records for swapping by evaluating item similarity in the transaction records.…”