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نتیجه جستجو - حفظ حریم خصوصی داده کاوی

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 A Cryptographic Ensemble for secure third party data analysis: Collaborative data clustering without data owner participation
یک گروه رمزنگاری برای تجزیه و تحلیل داده های شخص ثالث امن: خوشه بندی داده های مشارکتی بدون مشارکت صاحب داده-2019
This paper introduces the twin concepts Cryptographic Ensembles and Global Encrypted Distance Matrices (GEDMs), designed to provide a solution to outsourced secure collaborative data clustering. The cryptographic ensemble comprises: Homomorphic Encryption (HE) to preserve raw data privacy, while supporting data analytics; and Multi-User Order Preserving Encryption (MUOPE) to preserve the privacy of the GEDM. Clustering can therefore be conducted over encrypted datasets without requiring decryption or the involvement of data owners once encryption has taken place, all with no loss of accuracy. The GEDM concept is applicable to large scale collaborative data mining applications that feature horizontal data partitioning. In the paper DBSCAN clustering is adopted for illustrative and evaluation purposes. The results demonstrate that the proposed solution is both efficient and accurate while maintaining data privacy.
Keywords: Data mining as a service | Privacy preserving data mining | Security | Data outsourcing
مقاله انگلیسی
2 Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction
کاوش مجموعه موارد تکراری حفظ حریم خصوصی: حداکثر سازی تسهیل داده ها براساس بازسازی بانک اطلاعاتی-2019
The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible. Privacy preserving frequent itemset mining (PPFIM) has thus drawn increasing attention recently, where the ultimate goal is to hide sensitive frequent itemsets (SFIs) so as to leave no confidential knowledge uncovered in the resulting database. Nevertheless, the vast majority of the proposed methods for PPFIM were merely based on database per- turbation, which may result in a significant loss of data utility in order to conceal all SFIs. To alleviate this issue, this paper proposes a database reconstruction-based algorithm for PPFIM (DR-PPFIM) that can not only achieve a high degree of privacy but also afford a rea- sonable data utility. In DR-PPFIM, all SFIs with related frequent itemsets are first identified for removing in the pre-sanitize process by implementing a devised sanitize method. With the remained frequent itemsets, a novel database reconstruction scheme is proposed to re- construct an appropriate database, where the concepts of inverse frequent itemset mining (IFIM) and database extension are efficiently integrated. In this way, all SFIs are able to be hidden under the same mining threshold while maximizing the data utility of the synthetic database as much as possible. Moreover, we also develop a further hiding strategy in DRPPFIM to further decrease the significance of SFIs with the purpose of reducing the risk of disclosing confidential knowledge. Extensive comparative experiments are conducted on real databases to demonstrate the superiority of DR-PPFIM in terms of maximizing the utility of data and resisting potential threats.
Keywords: Privacy preserving data mining | Frequent itemset | Database reconstruction | Inverse frequent itemset mining | Database extension
مقاله انگلیسی
3 Efficient privacy preservation of big data for accurate data mining
حفظ حریم خصوصی کارا داده های بزرگ برای داده کاوی دقیق-2019
Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that po- tentially sensitive data may become public if the collected data are not appropriately sani- tized before being released for investigation. Although there are more than a few privacy- preserving methods available, they are not efficient, scalable, or have problems with data utility, or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via op- timal geometric transformations. PABIDOT was tested for efficiency, scalability, attack re- sistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance, and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.
Keywords: Information privacy | Privacy-preserving data mining | Big data privacy | Data perturbation | Big data
مقاله انگلیسی
4 Efficient data perturbation for privacy preserving and accurate data stream mining
اختلال در عملکرد داده ها برای حفظ حریم خصوصی و جریان کاوی داده های دقیق-2018
The widespread use of the Internet of Things (IoT) has raised many concerns, including the protection of private information. Existing privacy preservation methods cannot provide a good balance between data utility and privacy, and also have problems with efficiency and scalability. This paper proposes an efficient data stream perturbation method (named as P2RoCAl). P2RoCAl offers better data utility than similar methods and the classification accuracies of P2RoCAl perturbed data streams are very close to those of the original data streams. P2RoCAl also provides higher resilience against data reconstruction attacks.
Keywords: Privacy ،Privacy preserving data mining ، Data streams ، Internet of Things (IoT) ، Web of Things (WoT) ، Sensor data streams ، Big data
مقاله انگلیسی
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