Yavuz CANBAY, Yılmaz VURAL, Şeref SAĞIROĞLU

Mahremiyet korumalı büyük veriyayınlama için kavramsal model önerileri

Teknolojinin gelişmesi ile beraber veri üretim ve işleme hızı artmış,bunun sonucu olarak hacim, hız, çeşitlilik ve değer gibi bileşenlere sahip büyük veri kavramı ortaya çıkmıştır. Büyük verilerden elde edilecek faydayı arttırmak için bu verilerin mahremiyetini koruyarak paylaşmakveya yayınlamak gerekir. Literatür incelendiğinde, büyük verininmahremiyetini koruyarak yayınlanmasını kolaylaştıranherhangi bir modelin olmadığı tespit edilmiştir. Mahremiyet Korumalı Büyük Veri Yayınlama (Privacy Preserving Big Data Publishing –PPBDP) modellerinin oluşturulması, büyük veri mahremiyeti koruma sürecindeki tüm tarafların doğru bir şekilde yönlendirilmesi ve gereksinimlerinin doğru karşılanması, doğru alt yapı ve hizmetlerin oluşturulması adına önemlidir. Ayrıca, bu modelleri oluştururken maliyet ve güvenlik gibi faktörleri de göz önünde bulundurmak gerekir. Bu çalışmada, mahremiyet korumalı geleneksel veri yayınlama modelleri araştırılmış, çeşitli kriterlere göre karşılaştırılarakmahremiyet risk seviyeleri değerlendirilmiş ve bu risk seviyelerini de dikkate alan büyük veri temelli yeni kavramsal modeller ilk defaönerilmiştir. Önerilen bu modeller senaryo temelli olarak oluşturulmuş, üstünlükleri ve dezavantajları sunulmuştur. Önerilen modellerin, büyük verilerin mahremiyetinin korunarak yayınlanması, mahremiyet risklerinin minimize edilmesi ve büyük veriden maksimum faydanın sağlanması gibi pek çok konuda katkılar sağlayacağı değerlendirilmektedir.

Conceptual Model Suggestions for Privacy Preserving Big Data Publishing

Recent developments in IT has increased the speed of data production and processing, as a result, big dataconceptwith components such as volume, velocity, variety and valuehas emerged. In order to get more benefit from big data, it is necessary to share or publish the data by preserving or respecting privacy. The literature reviews report that there isno model that facilitatespublishing big data by preserving privacy. Designing Privacy Preserving Big Data Publishing (PPBDP) models is important to direct all the parties and to meet the requirements of themcorrectly, and to create the right infrastructures and services. In addition, it is necessary to consider some factors such as cost and security when designing these models. In this study, privacy preserving data publishing models were reviewed, compared based on various criteriaand then evaluated based on privacy risk levels. Finally,big data architecture based new conceptual models were then established for the first time according to these evaluations and privacy risk levels. It is expected that the proposed models might contribute to the literature on some issues, such as publishing big data withpreserving privacy, minimizing privacy risks and obtaining maximum benefit from the big data.

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[1]Warren S.D. and L.D. Brandeis, "The right to privacy", Harvard law review, 193-220, (1890).
[2]Beyer M.A. and Laney D., "The importance of ‘big data’: a definition", Stamford, CT: Gartner, (2012).
[3]Scott A., Srinivasan V., and Stege U., "k-Attribute-Anonymity is hard even for k=2", Information Processing Letters, 115(2): 368-370, (2015).
[4]Chibba M. and Cavoukian A., "Privacy, consumer trust and big data: Privacy by design and the 3 C’S", IEEE ITU Kaleidoscope: Trust in the Information Society, (2015).
[5]Jain P., Gyanchandani M., and Khare N., "Big data privacy: a technological perspective and review", Journal of Big Data, 3(1): 25, (2016).
[6]Zhang X., et al., "A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud", IEEE Transactions on Parallel and Distributed Systems, 25(2): 363-373, (2014).
[7]Fung B.C., et al., "Introduction to privacy-preserving data publishing: Concepts and techniques", CRC Press, (2010).
[8]Mehmood, A., et al., Protection of big data privacy. IEEE access, 4: 1821-1834, (2016).
[9]Zakerzadeh, H., Aggarwal, CC. and Barker, K., "Privacy-preserving big data publishing", International Conference on Scientific and Statistical Database Management, Kaliforniya, ABD, (2015).
[10]Machanavajjhala, A., et al., "l-diversity: Privacy beyond k-anonymity", IEEE International Conference on Data Engineering, Georgia, ABD, (2006).
[11]Nergiz, M.E., Atzori, M. and Clifton, C., "Hiding the presence of individuals from shared databases", ACM SIGMOD International Conference on Management of Data, Beijing, China (2007).
[12]Chen H., Chiang R.H., and Storey V.C., "Business intelligence and analytics: From big data to big impact", MIS, 36(4), (2012).
[13]Nandini K.S. and Pratheek T, "Providing anonymity using top down specialization on Big Data using hadoop framework", IEEE India Conference, India, (2015).
[14]Patil H.K. and Seshadri R., "Big data security and privacy issues in healthcare", IEEE International Congress on Big Data, Alaska, ABD, (2014).
[15]Victor N., Lopez D., and Abawajy J.H, "Privacy models for big data: a survey", International Journal of Big Data Intelligence, 3(1), 61-75, (2016).
[16]Zhang X., et al., "A MapReduce based approach of scalable multidimensional anonymization for big data privacy preservation on cloud", IEEE International Conference on Cloud and Green Computing,Karlsruhe, Germany,(2013).
[17]Li W. and Li H., "LRDM: Local Record-Driving Mechanism for Big Data Privacy Preservation in Social Networks", IEEE International Conference on Data Science in Cyberspace, Changsha, China, (2016).
[18]Olaronke I. and Oluwaseun O.,"Big data in healthcare: Prospects, challenges and resolutions", IEEE Future Technologies Conference, Kaliforniya, ABD, (2016).
[19]Tanwar M., Duggal R. and Khatri S.K, "Unravelling unstructured data: A wealth of information in big data. in Reliability",IEEE International Conference on Infocom Technologies and Optimization, Noida, India, (2015).
[20]Samadi Y., Zbakh M. and Tadonki C., "Comparative study between Hadoop and Spark based on Hibench benchmarks", International Conference on Cloud Computing Technologies and Applications, Marrakech, Morocco, (2016).
[21]Lee M.S., et al., "Design of educational big data application using spark", IEEE International Conference on Advanced Communication Technology, Bongpyeong, South Korea, (2017).
[22]İnternet: Apache Spark, http://spark.apache.org, (2017).
[23]Jam M.R., et al., "A survey on security of Hadoop", IEEE International Conference on Computer and Knowledge Engineering, Mashhad, Iran, (2014).
[24]Sogodekar M., et al., "Big data analytics: hadoop and tools", IEEE Bombay Section Symposium, Baramati, India, (2016).
[25]Fung B., et al., "Privacy-preserving data publishing: A survey of recent developments", ACM Computing Surveys, 42(4), 14, (2010).
[26]Jurczyk P. and Xiong L., "Distributed anonymization: Achieving privacy for both data subjects and data providers", Annual Conference on Data and Applications Security and Privacy, Montreal, Canada, (2009).
[27]Byun, J.W., et al., "Secure anonymization for incremental datasets", Workshop on Secure Data Management, Seoul, Korea, (2006).
[28]Wang, K. and Fung, B., "Anonymizing sequential releases", ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pensilvenya, ABD, (2006).
[29]Jurczyk, P. and Xiong, L., "Distributed Anonymization: Achieving Privacy for Both Data Subjects and Data Providers", DBSec, 5645: 191-207, (2009).
[30Vural, Y., "p-Gain: Privacy Preserving Utility-based Data Publishing Model", Ph. D. Thesis, Department of Computer Engineering, Hacettepe University, (2017).