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To preserve privacy, k-anonymity on relational, set-valued, and graph data have been studied extensively in recent years. Information on social networks can be modeled as un-weighted or weighted graph data for sharing and publishing. We have previously proposed k-anonymous path privacy concept on weighted social graphs to preserve privacy of the shortest path [9]. A published social network graph...
The privacy-preserving data mining (PPDM) has become an important issue in recent years. In this paper, we propose a lattice-based approach for modifying original databases in order to hide sensitive itemsets. The lattice structure is built based on the relation of sensitive itemsets. The approach uses the bottom-up deletion strategies to gradually reduce the frequency of sensitive itemsets in the...
The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data...
Many approaches for preserving association rule privacy, such as association rule mining outsourcing, association rule hiding, and anonymity, have been proposed. In particular, association rule hiding on single transaction table has been well studied. However, hiding multi-relational association rule in data warehouses is not yet investigated. This work presents a novel algorithm to hide predictive...
Current technology for association rules hiding mostly applies to data stored in a single transaction table. This work presents a novel algorithm for hiding sensitive association rules in data warehouses. A data warehouse is typically made up of multiple dimension tables and a fact table as in a star schema. Based on the strategies of reducing the confidence of sensitive association rule and without...
Data mining technology can help extract useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some sensitive or private information about individuals, businesses and organizations has to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important...
We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence...
We propose here an efficient data-mining algorithm to sanitize informative association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicting item, an informative association rule set [16] is the smallest association rule set that makes the same prediction as the entire association rule set by confidence priority. Several approaches...
We propose here a one-scan sanitization algorithm to hide informative association rules. For a given predicting item, an informative association rule set by Jiuyong Li et. al, (2001) is the smallest association rule set that makes the same prediction as the entire association rule set by confidence priority. To hide association rules, previously proposed algorithms based on a priori approach require...
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