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Conceptual Model Suggestions for Privacy Preserving Big Data Publishing

Year 2020, Volume: 23 Issue: 3, 785 - 798, 01.09.2020
https://doi.org/10.2339/politeknik.535184

Abstract

Recent developments in IT has increased the speed of
data production and processing, as a result, big data concept with components
such as volume, velocity, variety and value has 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 is
no model that facilitates publishing 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 them correctly, 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 criteria and 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 with preserving privacy,
minimizing privacy risks and obtaining maximum benefit from the big data.

References

  • Warren S.D. and L.D. Brandeis, "The right to privacy", Harvard law review, 193-220, (1890).
  • Beyer M.A. and Laney D., "The importance of ‘big data’: a definition", Stamford, CT: Gartner, (2012).
  • Scott A., Srinivasan V., and Stege U., "k-Attribute-Anonymity is hard even for k= 2", Information Processing Letters, 115(2), 368-370, (2015).
  • 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).
  • Jain P., Gyanchandani M., and Khare N., "Big data privacy: a technological perspective and review", Journal of Big Data, 3(1), 25, (2016).
  • 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).
  • Fung B.C., et al., "Introduction to privacy-preserving data publishing: Concepts and techniques", CRC Press, (2010).
  • Chen H., Chiang R.H., and Storey V.C., "Business intelligence and analytics: From big data to big impact", MIS, 36(4), (2012).
  • Nandini K.S. and Pratheek T, "Providing anonymity using top down specialization on Big Data using hadoop framework", IEEE India Conference, (2015).
  • Patil H.K. and Seshadri R., "Big data security and privacy issues in healthcare", IEEE International Congress on Big Data, (2014).
  • 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).
  • 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, (2013).
  • 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, (2016).
  • Olaronke I. and Oluwaseun O., "Big data in healthcare: Prospects, challenges and resolutions", IEEE Future Technologies Conference, (2016).
  • 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, (2015).
  • 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, (2016).
  • Lee M.S., et al., "Design of educational big data application using spark", IEEE International Conference on Advanced Communication Technology, (2017).
  • İnternet: Apache Spark, http://spark.apache.org/, (2017).
  • Jam M.R., et al., "A survey on security of Hadoop", IEEE International Conference on Computer and Knowledge Engineering, (2014).
  • Sogodekar M., et al., "Big data analytics: hadoop and tools", IEEE Bombay Section Symposium, (2016).
  • Fung B., et al., "Privacy-preserving data publishing: A survey of recent developments", ACM Computing Surveys, 42(4), 14, (2010).
  • 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, (2009).
  • Vural Y., "p-kazanım: Mahremiyet korumalı fayda temelli veri yayınlama modeli", Doktora Tezi, Hacettepe Üniversitesi, (2017).
  • Mehmood A., et al., "Protection of big data privacy", IEEE Access, 4, 1821-1834, (2016).
  • Zakerzadeh H., Aggarwal C.C., and Barker K., "Privacy-preserving big data publishing", ACM International Conference on Scientific and Statistical Database Management, (2015)
  • Nergiz M.E., Atzori M., and Clifton C., "Hiding the presence of individuals from shared databases", ACM SIGMOD International Conference on Management of Data, (2007).

Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri

Year 2020, Volume: 23 Issue: 3, 785 - 798, 01.09.2020
https://doi.org/10.2339/politeknik.535184

Abstract

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 paylaşmak veya yayınlamak gerekir. Literatür
incelendiğinde, büyük verinin mahremiyetini koruyarak yayınlanmasını kolaylaştıran
herhangi 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 karşılaştırılarak mahremiyet risk seviyeleri değerlendirilmiş
ve bu risk seviyelerini de dikkate alan büyük veri temelli yeni kavramsal modeller
ilk defa önerilmiştir. 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.  

References

  • Warren S.D. and L.D. Brandeis, "The right to privacy", Harvard law review, 193-220, (1890).
  • Beyer M.A. and Laney D., "The importance of ‘big data’: a definition", Stamford, CT: Gartner, (2012).
  • Scott A., Srinivasan V., and Stege U., "k-Attribute-Anonymity is hard even for k= 2", Information Processing Letters, 115(2), 368-370, (2015).
  • 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).
  • Jain P., Gyanchandani M., and Khare N., "Big data privacy: a technological perspective and review", Journal of Big Data, 3(1), 25, (2016).
  • 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).
  • Fung B.C., et al., "Introduction to privacy-preserving data publishing: Concepts and techniques", CRC Press, (2010).
  • Chen H., Chiang R.H., and Storey V.C., "Business intelligence and analytics: From big data to big impact", MIS, 36(4), (2012).
  • Nandini K.S. and Pratheek T, "Providing anonymity using top down specialization on Big Data using hadoop framework", IEEE India Conference, (2015).
  • Patil H.K. and Seshadri R., "Big data security and privacy issues in healthcare", IEEE International Congress on Big Data, (2014).
  • 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).
  • 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, (2013).
  • 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, (2016).
  • Olaronke I. and Oluwaseun O., "Big data in healthcare: Prospects, challenges and resolutions", IEEE Future Technologies Conference, (2016).
  • 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, (2015).
  • 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, (2016).
  • Lee M.S., et al., "Design of educational big data application using spark", IEEE International Conference on Advanced Communication Technology, (2017).
  • İnternet: Apache Spark, http://spark.apache.org/, (2017).
  • Jam M.R., et al., "A survey on security of Hadoop", IEEE International Conference on Computer and Knowledge Engineering, (2014).
  • Sogodekar M., et al., "Big data analytics: hadoop and tools", IEEE Bombay Section Symposium, (2016).
  • Fung B., et al., "Privacy-preserving data publishing: A survey of recent developments", ACM Computing Surveys, 42(4), 14, (2010).
  • 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, (2009).
  • Vural Y., "p-kazanım: Mahremiyet korumalı fayda temelli veri yayınlama modeli", Doktora Tezi, Hacettepe Üniversitesi, (2017).
  • Mehmood A., et al., "Protection of big data privacy", IEEE Access, 4, 1821-1834, (2016).
  • Zakerzadeh H., Aggarwal C.C., and Barker K., "Privacy-preserving big data publishing", ACM International Conference on Scientific and Statistical Database Management, (2015)
  • Nergiz M.E., Atzori M., and Clifton C., "Hiding the presence of individuals from shared databases", ACM SIGMOD International Conference on Management of Data, (2007).
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Yavuz Canbay 0000-0003-2316-7893

Yılmaz Vural This is me 0000-0002-2858-5448

Şeref Sağıroğlu 0000-0003-0805-5818

Publication Date September 1, 2020
Submission Date March 4, 2019
Published in Issue Year 2020 Volume: 23 Issue: 3

Cite

APA Canbay, Y., Vural, Y., & Sağıroğlu, Ş. (2020). Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri. Politeknik Dergisi, 23(3), 785-798. https://doi.org/10.2339/politeknik.535184
AMA Canbay Y, Vural Y, Sağıroğlu Ş. Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri. Politeknik Dergisi. September 2020;23(3):785-798. doi:10.2339/politeknik.535184
Chicago Canbay, Yavuz, Yılmaz Vural, and Şeref Sağıroğlu. “Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri”. Politeknik Dergisi 23, no. 3 (September 2020): 785-98. https://doi.org/10.2339/politeknik.535184.
EndNote Canbay Y, Vural Y, Sağıroğlu Ş (September 1, 2020) Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri. Politeknik Dergisi 23 3 785–798.
IEEE Y. Canbay, Y. Vural, and Ş. Sağıroğlu, “Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri”, Politeknik Dergisi, vol. 23, no. 3, pp. 785–798, 2020, doi: 10.2339/politeknik.535184.
ISNAD Canbay, Yavuz et al. “Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri”. Politeknik Dergisi 23/3 (September 2020), 785-798. https://doi.org/10.2339/politeknik.535184.
JAMA Canbay Y, Vural Y, Sağıroğlu Ş. Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri. Politeknik Dergisi. 2020;23:785–798.
MLA Canbay, Yavuz et al. “Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri”. Politeknik Dergisi, vol. 23, no. 3, 2020, pp. 785-98, doi:10.2339/politeknik.535184.
Vancouver Canbay Y, Vural Y, Sağıroğlu Ş. Mahremiyet Korumalı Büyük Veri Yayınlama İçin Kavramsal Model Önerileri. Politeknik Dergisi. 2020;23(3):785-98.