Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, , 355 - 368, 25.10.2019

Yavuz Canbay , Yılmaz Vural Şeref Sağıroğlu

https://doi.org/10.17341/gazimmfd.467390
Cited By: 1

Öz

Kaynakça

  • 1. Sweeney L. Simple demographics often identify people uniquely. https://dataprivacylab.org. Yayın tarihi 2000. Erişim tarihi Mart 19, 2018.
  • 2. Machanavajjhala A., Gehrke J., Kifer D., Venkitasubramaniam M., l-diversity: Privacy beyond k-anonymity, IEEE International Conference on Data Engineering, Atlanta-ABD, 24-24, 3-8 Nisan, 2006.
  • 3. Motwani R., Nabar S.U., Anonymizing unstructured data, arXiv:0810.5582, 2008.
  • 4. Fung B.C.M, Wang K., Fu A.W., Yu P.S., Introduction to Privacy-preserving Data Publishing: Concepts and Techniques, CRC Press, 2010.
  • 5. Majeed A., Attribute-centric Anonymization Scheme for Improving User Privacy and Utility of Publishing e-health Data, Journal of King Saud University-Computer and Information Sciences, basımda, 2018.
  • 6. Ramana K.V., Kumari V.V., Raju K., Impact of Outliers on Anonymized Categorical Data, International Conference on Advances in Digital Image Processing and Information Technology, Tirunelveli-Hindistan, 326-335, 23-25 Eylül, 2011.
  • 7. Wang H.W., Liu R., Hiding Distinguished Ones into Crowd: Privacy-preserving Publishing Data with Outliers, International Conference on Extending Database Technology: Advances in Database Technology, Saint-Petersburg-Russian, 624-635, 23-26 Mart, 2009.
  • 8. Wang H.W., Liu R., Hiding Outliers into Crowd: Privacy-preserving Data Publishing with Outliers, Data & Knowledge Engineering, 100, 94-115, 2015.
  • 9. Vural Y., ρ-Kazanım: Mahremiyet Korumalı Fayda Temelli Veri Yayınlama Modeli, Doktora Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, 2017.
  • 10. Vural Y., Aydos M., A New Approach to Utility-Based Privacy Preserving in Data Publishing, IEEE International Conference on Computer and Information Technology, Dakka-Bangladeş, 204-209, 22-24 Aralık, 2017.
  • 11. Vural Y., Aydos M., ρ-Gain: Utility Based Data Publishing Model, Journal of the Faculty of Engineering and Architecture of Gazi University, 2018 (18-1), 1-17, 2018.
  • 12. Lee H., Kim S., Kim J.W., Chung Y.D., Utility-preserving Anonymization for Health Data Publishing. BMC Medical Informatics and Decision Making, 17(1), 104-116, 2017.
  • 13. Breunig M.M., Kriegel H., Ng R.T., Sander J., LOF: Identifying Density-based Local Outliers, ACM International Conference on Management of Data, Teksas-ABD, 93-104, 16-18 Mayıs, 2000.
  • 14. Fung B.C.M, Wang K., Chen R., Yu P.S, Privacy-preserving Data Publishing: A Survey of Recent Developments, ACM Computing Surveys, 42(4), 1-53, 2010.
  • 15. Wong R.C., Fu A.W., Wang K., Pei J., Minimality Attack in Privacy Preserving Data Publishing, International Conference on Very Large Databases, Viyana-Avusturya, 543-554, 23-23 Eylül, 2007.
  • 16. Duncan G., Lambert D., The Risk of Disclosure for Microdata, Journal of Business & Economic Statistics, 7(2), 207-217, 1989.
  • 17. Chen B., LeFevre K., Ramakrishnan R., Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge, International Conference on Very Large Databases, Viyana-Avusturya, 543-554, 23-27 Eylül, 2007.
  • 18. Sweeney L., Computational Disclosure Control: A Primer on Data Privacy Protection, Doktora Tezi, Massachusetts Institute of Technology, Deptartment of Electrical Engineering and Computer Science, Massachusetts, 2001.
  • 19. Nergiz M.E., Atzori M., Clifton C., Hiding the Presence of Individuals from Shared Databases, ACM International Conference on Management of Data, Beijing-Çin, 665-676, 11-14 Haziran, 2007.
  • 20. Fang W., Wen X.Z., Zheng Y., Zhou M., A Survey of Big Data Security and Privacy Preserving, IETE Technical Review, 34(5), 544-560, 2017.
  • 21. Xu Y., Ma T., Tang M., Tian W., A Survey of Privacy Preserving Data Publishing Using Generalization and Suppression, Applied Mathematics & Information Sciences, 8(3), 1103-1116, 2014.
  • 22. Ye Y., Wang L., Han J., Qiu S., Luo F., An Anonymization Method Combining Anatomy and Permutation for Protecting Privacy in Microdata with Multiple Sensitive Attributes, IEEE International Conference on Machine Learning and Cybernetics, Ningbo-Çin, 404-411, 9-12 Haziran, 2017.
  • 23. Rahimi M., Bateni M., Mohammadinejad H., Extended k-anonymity Model for Privacy Preserving on Micro Data, International Journal of Computer Network and Information Security, 7(12), 42-51, 2015.
  • 24. Lin W., Yang D., Wang J., Privacy Preserving Data Anonymization of Spontaneous ADE Reporting System Dataset, BMC Medical Informatics and Decision Making, 16(1), 21-35, 2016.
  • 25. Sweeney L., k-anonymity: A Model for Protecting Privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557-570, 2002.
  • 26. Meyerson A., Williams R., On the Complexity of Optimal k-anonymity, ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Paris-Fransa, 223-228, 14-16 Haziran, 2004.
  • 27. Li N., Li T., Venkatasubramanian S., t-closeness: Privacy Beyond k-anonymity and l-diversity, IEEE International Conference on Data Engineering, İstanbul-Türkiye,106-115, 15-20 Nisan, 2007.
  • 28. Li N., Li T., Venkatasubramanian S., Closeness: A New Privacy Measure for Data Publishing, IEEE Transactions on Knowledge and Data Engineering, 22(7), 943-956, 2010.
  • 29. Sweeney L., Achieving k-anonymity Privacy Protection Using Generalization and Suppression, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 571-588, 2002.
  • 30. LeFevre K., DeWitt D.J., Ramakrishnan R., Incognito: Efficient Full-domain k-anonymity, ACM SIGMOD International Conference on Management of Data, Maryland-ABD, 49-60, 14-16 Haziran, 2005.
  • 31. Kohlmayer F., Prasser F., Eckert C., Kemper A., Kuhn K.A., Flash: Efficient, Stable and Optimal k-anonymity, IEEE International Conference on Privacy, Security, Risk and Trust and International Confernece on Social Computing, Amsterdam-Hollanda, 708-717, 3-5 Eylül, 2012. 32. Sweeney L., Datafly: A System for Providing Anonymity in Medical Data, Database Security XI, IFIP Advances in Information and Communication Technology, Massachusetts, Springer, 356-381, 1998.
  • 33. Wang K., Yu P.S., Chakraborty S., Bottom-up Generalization: A Data Mining Solution to Privacy Protection, IEEE International Conference on Data Mining, Bringhton-İngilitere, 249-256, 1-4 Kasım, 2004.
  • 34. Fung B.C.M, Wang K., Yu P.S., Top-Down Specialization for Information and Privacy Preservation, International Conference on Data Engineering. Tokyo-Japonya, 205-216, 5-8 Nisan, 2005.
  • 35. LeFevre K., DeWitt D.J., Ramakrishnan R., Mondrian Multidimensional k-anonymity, IEEE International Conference on Data Engineering, Atlanta-ABD, 25-25, 3-7 Nisan, 2006.
  • 36. Xiao X., Tao Y., Personalized Privacy Preservation, ACM SIGMOD International Conference on Management of Data, Şikago-ABD, 229-240, 27-29 Haziran, 2006.
  • 37. Samarati P., Protecting Respondents Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, 13(6), 1010-1027, 2001.
  • 38. Skowron A., Rauszer C., The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support, Cilt 11, Springer, 331-362, 1992.
  • 39. Aggarwal C.C., Outlier Analysis, Springer, Cham, 2017.
  • 40. Han J., Pei J., Kamber M., Data Mining: Concepts and Techniques, Elsevier, 2011.
  • 41. Witten I.H., Frank E., Hall M.A., Pal C.J., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016.
  • 42. Dheeru D., Taniskidou E.K. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Yayın tarihi 2017, Erişim tarihi Mart 25, 2018.

OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli

Yıl 2020, , 355 - 368, 25.10.2019

Yavuz Canbay , Yılmaz Vural Şeref Sağıroğlu

https://doi.org/10.17341/gazimmfd.467390
Cited By: 1

Öz

Veri
mahremiyeti, mahremiyet riskleri ile veriden sağlanan fayda arasındaki en iyi
dengeyi bulmaya çalışan zor bir problemdir. Anonimleştirme, veri mahremiyetinin
sağlanmasında yaygın olarak kullanılan fayda temelli çözümlerin başında gelir.
Mahremiyet risklerini arttıran ve veri faydasını olumsuz etkileyen aykırı
kayıtların anonimleştirme sürecinde yönetilmesi gerekir. Geleneksel
yaklaşımlarda aykırı kayıtlar, anonimleştirme sonrası tespit edilerek
mahremiyet risklerini düşürmek amacıyla yayınlanacak veri kümesinden kısmen
veya tamamen çıkarılır. Aykırı kayıtların yayınlanacak veri kümesinden
çıkarılması veriden elde edilecek toplam veri faydasını düşürürken, bu
kayıtların anonimleştirme sonrası tespit edilmesi ise hesaplama maliyetini
arttırır. Bu çalışmada, aykırı kayıtları anonimleştirme öncesi tespit ederek
hesaplama maliyetini düşüren ve tüm kayıtları kullanarak veri faydasını
arttıran aykırı kayıt yönelimli fayda temelli OAN adı verilen yeni bir
mahremiyet koruma modeli önerilmiştir. OAN modelinin hesaplama maliyeti
açısından etkin bir çözüm olduğu, fayda temelli geliştirilen ilk modelle
kıyaslanarak gösterilmiştir. Yapılan deneysel çalışmalara göre, önerilen
modelin veri mahremiyetini koruyarak toplam veri faydasını arttırdığı
gözlemlenmiştir. 

Anahtar Kelimeler

Veri faydası aykırı kayıt yönetimi mahremiyet koruma

Kaynakça

  • 1. Sweeney L. Simple demographics often identify people uniquely. https://dataprivacylab.org. Yayın tarihi 2000. Erişim tarihi Mart 19, 2018.
  • 2. Machanavajjhala A., Gehrke J., Kifer D., Venkitasubramaniam M., l-diversity: Privacy beyond k-anonymity, IEEE International Conference on Data Engineering, Atlanta-ABD, 24-24, 3-8 Nisan, 2006.
  • 3. Motwani R., Nabar S.U., Anonymizing unstructured data, arXiv:0810.5582, 2008.
  • 4. Fung B.C.M, Wang K., Fu A.W., Yu P.S., Introduction to Privacy-preserving Data Publishing: Concepts and Techniques, CRC Press, 2010.
  • 5. Majeed A., Attribute-centric Anonymization Scheme for Improving User Privacy and Utility of Publishing e-health Data, Journal of King Saud University-Computer and Information Sciences, basımda, 2018.
  • 6. Ramana K.V., Kumari V.V., Raju K., Impact of Outliers on Anonymized Categorical Data, International Conference on Advances in Digital Image Processing and Information Technology, Tirunelveli-Hindistan, 326-335, 23-25 Eylül, 2011.
  • 7. Wang H.W., Liu R., Hiding Distinguished Ones into Crowd: Privacy-preserving Publishing Data with Outliers, International Conference on Extending Database Technology: Advances in Database Technology, Saint-Petersburg-Russian, 624-635, 23-26 Mart, 2009.
  • 8. Wang H.W., Liu R., Hiding Outliers into Crowd: Privacy-preserving Data Publishing with Outliers, Data & Knowledge Engineering, 100, 94-115, 2015.
  • 9. Vural Y., ρ-Kazanım: Mahremiyet Korumalı Fayda Temelli Veri Yayınlama Modeli, Doktora Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, 2017.
  • 10. Vural Y., Aydos M., A New Approach to Utility-Based Privacy Preserving in Data Publishing, IEEE International Conference on Computer and Information Technology, Dakka-Bangladeş, 204-209, 22-24 Aralık, 2017.
  • 11. Vural Y., Aydos M., ρ-Gain: Utility Based Data Publishing Model, Journal of the Faculty of Engineering and Architecture of Gazi University, 2018 (18-1), 1-17, 2018.
  • 12. Lee H., Kim S., Kim J.W., Chung Y.D., Utility-preserving Anonymization for Health Data Publishing. BMC Medical Informatics and Decision Making, 17(1), 104-116, 2017.
  • 13. Breunig M.M., Kriegel H., Ng R.T., Sander J., LOF: Identifying Density-based Local Outliers, ACM International Conference on Management of Data, Teksas-ABD, 93-104, 16-18 Mayıs, 2000.
  • 14. Fung B.C.M, Wang K., Chen R., Yu P.S, Privacy-preserving Data Publishing: A Survey of Recent Developments, ACM Computing Surveys, 42(4), 1-53, 2010.
  • 15. Wong R.C., Fu A.W., Wang K., Pei J., Minimality Attack in Privacy Preserving Data Publishing, International Conference on Very Large Databases, Viyana-Avusturya, 543-554, 23-23 Eylül, 2007.
  • 16. Duncan G., Lambert D., The Risk of Disclosure for Microdata, Journal of Business & Economic Statistics, 7(2), 207-217, 1989.
  • 17. Chen B., LeFevre K., Ramakrishnan R., Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge, International Conference on Very Large Databases, Viyana-Avusturya, 543-554, 23-27 Eylül, 2007.
  • 18. Sweeney L., Computational Disclosure Control: A Primer on Data Privacy Protection, Doktora Tezi, Massachusetts Institute of Technology, Deptartment of Electrical Engineering and Computer Science, Massachusetts, 2001.
  • 19. Nergiz M.E., Atzori M., Clifton C., Hiding the Presence of Individuals from Shared Databases, ACM International Conference on Management of Data, Beijing-Çin, 665-676, 11-14 Haziran, 2007.
  • 20. Fang W., Wen X.Z., Zheng Y., Zhou M., A Survey of Big Data Security and Privacy Preserving, IETE Technical Review, 34(5), 544-560, 2017.
  • 21. Xu Y., Ma T., Tang M., Tian W., A Survey of Privacy Preserving Data Publishing Using Generalization and Suppression, Applied Mathematics & Information Sciences, 8(3), 1103-1116, 2014.
  • 22. Ye Y., Wang L., Han J., Qiu S., Luo F., An Anonymization Method Combining Anatomy and Permutation for Protecting Privacy in Microdata with Multiple Sensitive Attributes, IEEE International Conference on Machine Learning and Cybernetics, Ningbo-Çin, 404-411, 9-12 Haziran, 2017.
  • 23. Rahimi M., Bateni M., Mohammadinejad H., Extended k-anonymity Model for Privacy Preserving on Micro Data, International Journal of Computer Network and Information Security, 7(12), 42-51, 2015.
  • 24. Lin W., Yang D., Wang J., Privacy Preserving Data Anonymization of Spontaneous ADE Reporting System Dataset, BMC Medical Informatics and Decision Making, 16(1), 21-35, 2016.
  • 25. Sweeney L., k-anonymity: A Model for Protecting Privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557-570, 2002.
  • 26. Meyerson A., Williams R., On the Complexity of Optimal k-anonymity, ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Paris-Fransa, 223-228, 14-16 Haziran, 2004.
  • 27. Li N., Li T., Venkatasubramanian S., t-closeness: Privacy Beyond k-anonymity and l-diversity, IEEE International Conference on Data Engineering, İstanbul-Türkiye,106-115, 15-20 Nisan, 2007.
  • 28. Li N., Li T., Venkatasubramanian S., Closeness: A New Privacy Measure for Data Publishing, IEEE Transactions on Knowledge and Data Engineering, 22(7), 943-956, 2010.
  • 29. Sweeney L., Achieving k-anonymity Privacy Protection Using Generalization and Suppression, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 571-588, 2002.
  • 30. LeFevre K., DeWitt D.J., Ramakrishnan R., Incognito: Efficient Full-domain k-anonymity, ACM SIGMOD International Conference on Management of Data, Maryland-ABD, 49-60, 14-16 Haziran, 2005.
  • 31. Kohlmayer F., Prasser F., Eckert C., Kemper A., Kuhn K.A., Flash: Efficient, Stable and Optimal k-anonymity, IEEE International Conference on Privacy, Security, Risk and Trust and International Confernece on Social Computing, Amsterdam-Hollanda, 708-717, 3-5 Eylül, 2012. 32. Sweeney L., Datafly: A System for Providing Anonymity in Medical Data, Database Security XI, IFIP Advances in Information and Communication Technology, Massachusetts, Springer, 356-381, 1998.
  • 33. Wang K., Yu P.S., Chakraborty S., Bottom-up Generalization: A Data Mining Solution to Privacy Protection, IEEE International Conference on Data Mining, Bringhton-İngilitere, 249-256, 1-4 Kasım, 2004.
  • 34. Fung B.C.M, Wang K., Yu P.S., Top-Down Specialization for Information and Privacy Preservation, International Conference on Data Engineering. Tokyo-Japonya, 205-216, 5-8 Nisan, 2005.
  • 35. LeFevre K., DeWitt D.J., Ramakrishnan R., Mondrian Multidimensional k-anonymity, IEEE International Conference on Data Engineering, Atlanta-ABD, 25-25, 3-7 Nisan, 2006.
  • 36. Xiao X., Tao Y., Personalized Privacy Preservation, ACM SIGMOD International Conference on Management of Data, Şikago-ABD, 229-240, 27-29 Haziran, 2006.
  • 37. Samarati P., Protecting Respondents Identities in Microdata Release, IEEE Transactions on Knowledge and Data Engineering, 13(6), 1010-1027, 2001.
  • 38. Skowron A., Rauszer C., The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support, Cilt 11, Springer, 331-362, 1992.
  • 39. Aggarwal C.C., Outlier Analysis, Springer, Cham, 2017.
  • 40. Han J., Pei J., Kamber M., Data Mining: Concepts and Techniques, Elsevier, 2011.
  • 41. Witten I.H., Frank E., Hall M.A., Pal C.J., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016.
  • 42. Dheeru D., Taniskidou E.K. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Yayın tarihi 2017, Erişim tarihi Mart 25, 2018.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Yavuz Canbay 0000-0003-2316-7893

Yılmaz Vural Bu kişi benim 0000-0002-2858-5448

Şeref Sağıroğlu 0000-0003-0805-5818

Yayımlanma Tarihi 25 Ekim 2019
Gönderilme Tarihi 4 Ekim 2018
Kabul Tarihi 6 Mart 2019
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Canbay, Y., Vural, Y., & Sağıroğlu, Ş. (2019). OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(1), 355-368. https://doi.org/10.17341/gazimmfd.467390
AMA Canbay Y, Vural Y, Sağıroğlu Ş. OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli. GUMMFD. Ekim 2019;35(1):355-368. doi:10.17341/gazimmfd.467390
Chicago Canbay, Yavuz, Yılmaz Vural, ve Şeref Sağıroğlu. “OAN: Aykırı kayıt yönelimli Fayda Temelli Mahremiyet Koruma Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35, sy. 1 (Ekim 2019): 355-68. https://doi.org/10.17341/gazimmfd.467390.
EndNote Canbay Y, Vural Y, Sağıroğlu Ş (01 Ekim 2019) OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35 1 355–368.
IEEE Y. Canbay, Y. Vural, ve Ş. Sağıroğlu, “OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli”, GUMMFD, c. 35, sy. 1, ss. 355–368, 2019, doi: 10.17341/gazimmfd.467390.
ISNAD Canbay, Yavuz vd. “OAN: Aykırı kayıt yönelimli Fayda Temelli Mahremiyet Koruma Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 35/1 (Ekim 2019), 355-368. https://doi.org/10.17341/gazimmfd.467390.
JAMA Canbay Y, Vural Y, Sağıroğlu Ş. OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli. GUMMFD. 2019;35:355–368.
MLA Canbay, Yavuz vd. “OAN: Aykırı kayıt yönelimli Fayda Temelli Mahremiyet Koruma Modeli”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 35, sy. 1, 2019, ss. 355-68, doi:10.17341/gazimmfd.467390.
Vancouver Canbay Y, Vural Y, Sağıroğlu Ş. OAN: aykırı kayıt yönelimli fayda temelli mahremiyet koruma modeli. GUMMFD. 2019;35(1):355-68.

Cited By

Derin Öğrenmede Diferansiyel Mahremiyet
ULUSLARARASI BİLGİ GÜVENLİĞİ MÜHENDİSLİĞİ DERGİSİ
Yavuz CANBAY
https://doi.org/10.18640/ubgmd.750310