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IRJET- Study Paper on: Ontology-based Privacy Data Chain Disclosure Discovery Method for Big Data
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1509 Study Paper On: Ontology-Based Privacy Data Chain Disclosure Discovery Method for Big Data Sonali T. Benke1, Devidas S.Thosar2, Kishor N. Shedage3 1 M.E. Student, Computer Engineering, SVIT, Nashik 2PG Co-ordinator, Computer Engineering, SVIT, Nashik 3HOD, Computer Engineering, SVIT, Nashik ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - As a new software paradigm, cloud computing provides services dynamically accordingtouserrequirements. However, it tends to disclose personal information due to collaborative computing and transparent interactionsamong SaaS services. We propose a private data disclosure checking method that can be applied to the collaboration interaction process. First, we describe the privacy requirement with ontology and description logic. Second, with dynamic description logic, we validate whether SaaS services are authorized to obtain a user’s privacy attributes, to prevent unauthorized services from obtaining their private data. Third, we monitor authorized SaaS services to guarantee privacy requirements. Therefore, wecanpreventusers’private data from being used and propagated illegally. Finally, we propose privacy disclosure checking algorithms and demonstrate their correctness and feasibility by experiments[7]. To meet user's functional requirements, cloud computing and big data have become themostcommonly usedcomputingand data resources. Based on analysis, conversion, extraction and refinement for the big data, a disease can be prevented and group behavior can be predicted. However, eachuser’sprivate data is also an element in big data. Users must provide private data to the service providers to meet their functional requirements. To gain economic benefits, some SaaS service providers have not been authorized to collect and analyze the user's sensitive private data, as a result, theuser’sprivatedata is disclosed. In this paper, we propose a private data chain disclosure discovery method, to prevent a user's sensitive privacy information from being illegally disclosed. Firstly, we measure the similarity degree and cost of the disclosure of the private data. Key Words: Ontology, Privacy Disclosure Detection, Privacy Data Chain, Similarity Metric etc. 1. INTRODUCTION Big data usually include data sets that have sizes that are beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time, with characteristics that consist of volume, variety and velocity.[18] According to statistics, an average of 2 million users per second use the Google search engine; within one second, Facebook users share information more than 4 billion times, and Twitter handles more than 3.4 hundred million tweets per day. The amount of data grows exponentially every year, and threequarters of data are produced by people, for example, a standard American worker contributes 1.8 million MB every year. A large amount of personal privacy data can be mined for commercial purposes by agents. For example, Acxiomac queries more than 5 million personal data of consumers all over the world through data processing and analyzes individual behaviors and psychological tendencies with technologies, some of which are known as data association and logical reasoning. In 2014, Adam Sadilekat University of Rochester and John Krumm in the Microsoft lab predicted a person’s likelihood to reach a location in the future by analyzingtheinformation in the big data, with an accuracy as high as 80%. A mobile application does not protect the location of the big data; as a result, a user's home address and other sensitive information can be disclosed through the triangulation reasoning method. Research shows that user attributes can be found by analyzing group featuresina social network. For example, by analyzing a user's Twitter messages, the user's political leanings, consumption habits and other personal preferences can be found. Therefore, how to protect personal privacy information has become a hot research topic with respect to bigdata. 1.1 Objective 1. We can check the privatedata disclosurechainandthekey private data, which can effectively prevent service participants from maliciously disclosing users' private data, increase the servicetrustworthiness,andprovideabasisfora privacy safety-oriented trustworthiness measurement. Detailed contributions are showed as follows. Firstly, we get the relationships among privacy data by the mapping with knowledge ontology, and build the ontology tree. We also measure the similarity degrees, containing property similarity, object similarity and hierarchical similarity. 2. Secondly, we measure the cost of the disclosure of the private data with sensitivity grades and privacy disclosure vector. According to the similarity degree and cost of disclosure, the disclosure chain and key private data are detected in the process of interaction between user and SaaS service.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1510 3. Thirdly, wepropose a discovery frameworkfortheprivate data chain and demonstrate its feasibility and effectiveness by experiments. Which provide a reference to develop a system software assuring thesafetyforpersonalprivacydata in big data. 1.2 Literature Survey A number of researches have been proposed by researchers for privacy preserving in big data.Adetailedsurveyhasbeen carried out to identify the various research articlesavailable in the literature in all the categories of privacy preserving in big data, and to do the analysis of the major contributions and its advantages. Following are the literatures applied for assessment of the state-of-art work on the privacy preserving in big data. Here, few works has been analyzed. Research article related to privacy preserving in data mining: Data mining is a strategy where huge measures of both sensitive and non-sensitive information are gathered and analyzed. While circulating such private information, security protecting turns into a critical issue. Different strategies and procedures have been presented in security saving information mining to attempt this issue. The techniques can be listed under three major classical PPDM techniques. Privacy preserving using Anonymization approach: Asmaa et al.[13] have explained the Protect Privacy of Medical Informatics using K-Anonymization Model. Here, they displayed a structure and model framework for de- distinguishing health data including both organized and unstructured information. They exactly examine a straightforward Bayesian classifier, a Bayesian classifier with an inspecting based strategy,anda contingentirregular field based classifier for removing distinguishing traitsfrom unstructured information. They, convey a k anonymization based system for de-recognizingthe removedinformationto save most extreme information utility. Moreover, Tiancheng Li et al. [14] have explained the Towards Optimal k- anonymization whichwasa moreflexibleschemeforprivacy preserving. Here, they introduced enumerate the algorithm for pruning approach for finding optimal generalization. Likewise, V.Rajalakshmi and G.S.Anandha Mala [11] amazingly advocated the Anonymization based on nested clustering for privacy preservation in Data Mining. In their document, the dimension of clusters was preserved optimal to cut down the data loss. They elaborately discussed the technique, performance and outcomes of the nested clustering.Similarly,YanZhuandLin Peng [15] have explained the Study on K-anonymity Models of Sharing Medical Information. Privacy preserving using Clustering algorithm: In this section, we discussed the research article based on privacy preserving using clustering algorithm. In S. Patel et al. proficiently introduced a privacy preserving distributed K-Means clustering of horizontally partitioned data which significantly alleviated the safety concerns in the malicious ill-disposed model. The vital growth involved the use of the secret transferring mechanism battered to code based zero knowledge identification technique. Moreover, Bipul Roy brilliantly brought to limelight an innovative tree-based perturbation approach which was easily employed for tackling the perturbing data reflecting the hidden conveyances. In their approach, they madeuseof a Kd-tree stratagem to recursively divide a dataset into a number of diminutive subsets in such a way that the data records within each subset becamefurtherharmonizedwith every partition. When the partitioning process was fully carried out the confidential data in every subset were perturbed by the effective exploitation of the micro aggregation method. Additionally, Alper Bilge and Huseyin Polat admirably brought to light the scalable privacy-preserving recommendation technique by means of bisecting k-means clustering. In their innovative privacy-preserving collaborative filtering scheme dependent on bisecting k- means clustering they introduced two pre-processing approaches. Similarly, Ali Inan et al. intelligently advocated the Privacy preserving clustering on horizontallypartitioneddata.Inthe document, they competently createdthedissimilaritymatrix of objects from varied sites in a privacy preserving fashion which was employed for the purpose of the privacy preserving clustering and also the database joins, record linkage and other function which necessitated couple wise appraisal and assessment of individual private data objects horizontally disseminated to several sites. In Jinfei Liu et al. efficiently brought to limelight the Privacy Preserving Distributed DBSCAN Clustering. In their innovative technique, they effectively tackled the hassles of the two-party privacy preserving DBSCAN clustering. At the outset, they brilliantly brought in two protocols for privacy preserving DBSCAN clustering over horizontally and vertically segregated data correspondingly and later widened them to the randomly segregated data. Also, Pan Yang et al. excellently explained the Privacy- Preserving Data Obfuscation Scheme Used in Data Statistics and Data Mining. In their technique, they apportioned dissimilar keys to the diverse users, who were given divergent permissions to access the data. In the ultimate phase, a fine-grained groupingmethodfoundedonsimilarity was discussed.
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of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1511 2. MOTIVATION Thanks to the advent of the Internet, it is now possible to easily share vast amounts of electronic information and computer resources (which include hardware, computer services, etc.) in open distributed environments. These environmentsserveasacommonplatformforheterogeneous users (e.g.,corporate, individuals etc.) byhostingcustomized user applicationsandsystems,providingubiquitousaccessto the shared resources and requiring less administrative efforts; as a result, they enable users and companies to increase their productivity.[9] Theseapproachesareusuallyenoughtomanageusers’access rights in closed environments, such as static organizations, which involve alimited numberofentitiesandresources,and for which a manual management of access rules is feasible. However, manually managing privacy rules and access constraints in open environment, such as OSNs or the cloud, is not practical due to the following reasons: (i) A large number of entities need to be managed. For example, Google Drive has billions of users and each user manages several types of resources. (ii) The heterogeneous entities involved in these scenarios would likely have diverse privacy requirements. For example, for a cloud provider offering storage space, an organization involving employees, departments and resources would define significantly different privacy requirements than casual end-users. (iii) The dynamicity and openness of such scenarios make the privacy requirements to change rapidly with respect to the type of services and users. Fig -1: System diagram Fig -2: block diagram 3. CONCLUSION According to the interaction charactersamongSaaSservices, we propose a privacy disclosure checking method that satisfies users’ requirements. We develop a prototype system, which describestheusers’privacyrequirementsand extends BPEL and its executionenginetomeetusers’privacy requirements. We also design a case and run it on the prototype system to confirm the feasibility and correctness of our method. Our approach can check privacy disclosure behavior among SaaS services, which caneffectivelyprevent service participants from maliciously disclosing users’ privacy information,increaseservicecredibility,andprovide a basis for privacy protection-oriented credibility measurement. The next step is to detect the release of the data of users’ privacy, analyze the data, and discretize the dataset that may be exposed to protect users’ privacybefore they are released. REFERENCES [1] Changbo Ke, Fu Xiao, Member, IEEE, Zhiqiu Huang, Yunfei Meng and Yan Cao ,Ontology-Based Privacy Data Chain Disclosure Discovery Method for Big Data”, 2019 IEEE. [2] Wang Y, Kung L A, Wang W Y C, et al. An integrated big data analyticsenabled transformation model: Application to health care [J]. Information & Management, 2018, 55(1):64- 79. [3] He X, Ai Q, Qiu R C, et al. A big data architecture designfor smart grids based on random matrix theory [J]. IEEE transactions on smart Grid, 2017, 8(2): 674-686. [4] Peddinti S T, Ross K W, Cappos J. User Anonymity on Twitter[J]. IEEE Security & Privacy, 2017, 15(3): 84-87.
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of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 1512 [5] Wan J, Tang S, Li D, et al. A manufacturing big data solution for active preventive maintenance [J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 2039- 2047. [6] Storey V C, Song I Y. Big data technologies and management: What conceptual modeling can do Data & Knowledge Engineering, 2017, 108: 50-67. [7] Ke C, Huang Z, Cheng X. Privacy Disclosure Checking Method Applied on Collaboration Interactions Among SaaS Services[J].IEEE Access, 2017, 5: 15080-15092. [8] Zeydan E, Bastug E, Bennis M, et al. Big data caching for networking: Moving from cloud to edge IEEE Communications Magazine, 2016, 54(9): 36-42. [9] Imran-Daud, Malik, “Ontology-based Access Control in Open Scenarios: Applications to Social Networks and the Cloud”, eprint arXiv: 1612.09527,Pub Date: December 2016 [10] Zhang X, Dou W, Pei J, et al. Proximity-aware local- recoding anonymization with mapreduce for scalable big data privacy preservation in cloud [J]. IEEE transactions on computers, 2015, 64(8): 2293-2307. [11] Lv Y, Duan Y, Kang W, et al. Traffic flow prediction with big data: A deep learning approach [J]. IEEE Trans. Intelligent Transportation Systems, 2015, 16(2): 865-873. [12] V Rajalaxmi,GSA MALA,Anonymization basedonnested clustering for privacy preservation in data mining,IJCSE, 2013 [13] Sankita Patel, Viren Patel, Devesh Jinwala, “Privacy preserving distributed” [14] Snijders, C.; Matzat, U.; Reips, U.-D. (2012). "'Big Data': Big gaps of knowledge in the field of Internet". International Journal of Internet Science 7: 1–5 [15] Asmaa H. Rashid, A. F.Hegazy, “Protect privacy of medical informatics using K- anonymization madel”, IEEE, April 2010 [16] Tiancheng Li, Ninghui Li, Towards optimal k- anonymization, Data & knowledge Engineering volume 65, issue 1, April 2008 pages 22-39 [17] Yan ZHU, Lin PENG, “Study on k-anonymity models of sharing medical information”, IEEE, 2007[ [18] www.wikipedia.com
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