privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. Many of the researchers in this area have recently made effort to preserve privacy for s...
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privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. Many of the researchers in this area have recently made effort to preserve privacy for sensitive association rules in statistical database. In this paper, we propose a heuristic algorithm named DSRRC (Decrease Support of R.H.S. item of Rule Clusters), which provides privacy for sensitive rules at certain level while ensuring data quality. Proposed algorithm clusters the sensitive association rules based on certain criteria and hides as many as possible rules at a time by modifying fewer transactions. Because of less modification in database it helps maintaining data quality.
Video cameras are being extensively used in many applications. Huge amounts of video are being recorded and stored everyday by surveillance systems. Any proposed application of this data raises severe privacy concerns...
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Video cameras are being extensively used in many applications. Huge amounts of video are being recorded and stored everyday by surveillance systems. Any proposed application of this data raises severe privacy concerns. An assessment of privacy loss is necessary before any potential application of the data. In traditional methods of privacy modeling, researchers have focused on explicit means of identity leakage like facial information, etc. However, other implicit inference channels through which individual's an identity can be learned have not been considered. For example, an adversary can observe the behavior, look at the places visited and combine that with the temporal information to infer the identity of the person in the video. In this work, we thoroughly investigate privacy issues involved with the video data considering both implicit and explicit channels. We first establish an analogy with the statisticaldatabases and then propose a model to calculate the privacy loss that might occur due to publication of the video data. The experimental results demonstrate the utility of the proposed model.
In our era, Knowledge is not ¿just¿ information anymore, it is an asset. Data mining is thus extensively used for knowledge discovery from large data bases. The problem with the data mining is that with the ...
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In our era, Knowledge is not ¿just¿ information anymore, it is an asset. Data mining is thus extensively used for knowledge discovery from large data bases. The problem with the data mining is that with the availability of non-sensitive information that is not to be disclosed. Thus privacy is becoming an increasingly important issue in many data mining applications. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. A number of techniques such as randomization and k-anonymity have been suggested in recent years in order to perform privacy-preserving data mining. Furthermore, the problem has been discussed in multiple communities such as the database community, the statistical disclosure control community and the cryptography community. We propose a new solution by integrating the advantages of both these techniques with the view of minimizing information loss and privacy loss. By making use of cryptographic techniques to store sensitive data and providing access to the stored data based on an individual's role, we ensure that the data is safe from privacy breaches. The trade-off between data utility and data safety of our proposed method will be assessed.
Microdata protection in statisticaldatabases has recently become a major societal concern and has been intensively studied in recent years. statistical Disclosure Control (SDC) is often applied to statistical databas...
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Microdata protection in statisticaldatabases has recently become a major societal concern and has been intensively studied in recent years. statistical Disclosure Control (SDC) is often applied to statisticaldatabases before they are released for public use. Micro aggregation for SDC is a family of methods to protect micro data from individual identification. SDC seeks to protect micro data in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Micro aggregation works by partitioning the micro data into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal micro aggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the micro aggregation process. This paper presents a pair wise systematic (P-S) micro aggregation method to minimize the information loss. The proposed technique simultaneously forms two distant groups at a time with the corresponding similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of P-S problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the P-S algorithm is compared against the most recent micro aggregation methods. Experimental results show that P-S algorithm incurs less than half information loss than the latest micro aggregation methods for all of the test situations.
Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster....
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ISBN:
(纸本)9783540882688
Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the cluster. Usually, when records are complex, i.e., the number of attributes of the data set, is large, this data set is split into smaller blocks of attributes and microaggregation is applied to each block, successively and independently. In this way, the information loss when collapsing several values to the centroid of their group is reduced, at the cost of losing the k-anonymity property when at least;two attributes of different blocks are known by the intruder. In this work, we present a new microaggregation method called One dimension microaggregation (Mic1D - kappa). This method gathers all the values of the data set into a single sorted vector, independently of the attribute they belong to. Then, it microaggregates all the mixed values together. Our experiments show that, using real data, our proposal obtains lower disclosure risk than previous approaches whereas the information loss is preserved.
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