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نتیجه جستجو - Privacy preserving data mining

تعداد مقالات یافته شده: 5
ردیف عنوان نوع
1 Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives
خوشه بندی حفظ حریم خصوصی برای داده های بزرگ در سیستم های سایبر-فیزیکی-اجتماعی: بررسی و چشم انداز-2020
Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized rec- ommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing meth- ods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and stor- age. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multi- ple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.
Keywords: CPSS | Big data | Cloud computing | Privacy preserving | Clustering
مقاله انگلیسی
2 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
3 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
4 Privacy preserving big data mining: association rule hiding using fuzzy logic approach
حفظ حریم خصوصی کاوش داده های بزرگ :پنهان سازی قوانین انجمنی با استفاده از رویکرد منطق فازی-2018
Recently, privacy preserving data mining has been studied widely. Association rule mining can cause potential threat toward privacy of data. So, association rule hiding techniques are employed to avoid the risk of sensitive knowledge leakage. Many researches have been done on association rule hiding, but most of them focus on proposing algorithms with least side effect for static databases (with no new data entrance), while now the authors confront with streaming data which are continuous data. Furthermore, in the age of big data, it is necessary to optimise existing methods to be executable for large volume of data. In this study, data anonymisation is used to fit the proposed model for big data mining. Besides, special features of big data such as velocity make it necessary to consider each rule as a sensitive association rule with an appropriate membership degree. Furthermore, parallelisation techniques which are embedded in the proposed model, can help to speed up data mining process.
Index Terms: authorisation, Big Data, data mining, data protection, fuzzy logic
مقاله انگلیسی
5 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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