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Bigdata Anonymization Using One Dimensional and Multidimensional Map Reduce Framework on Cloud
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- 발행기관
- 보안공학연구지원센터(IJDTA) 바로가기
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- 간행물
- International Journal of Database Theory and Application SCOPUS 바로가기
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- 통권
- Vol.8 No.6 (2015.12)바로가기
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- 페이지
- pp.253-262
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A267622
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Data privacy preservation is one of the most disturbed issues on the current industry. Data privacy issues need to be addressed urgently before data sets are shared on cloud. Data anonymization refers to as hiding complex data for owners of data records. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, the scale of data in many cloud applications increases tremendously in accordance with the big data trend. Here propose a scalable Two Phase Top-Down Specialization (TPTDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization on map reducing framework, here implemented the proposed Two-Phase Top-Down Specialization anonymization algorithm on hadoop will increases the efficiency of the big data processing system. In both phases of the approach, deliberately design multidientional MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Data sets are generalized in a top-down manner and the better result was shown in multidmientional MapReduce framework by compairing the onedimentional MapReduce framework anonymization job. The anonymization was performed with specialization operation on the taxonomy tree. The experiment demonstrates that the solutions can significantly improve the scalability and efficiency of big data privacy preservation compared to existing approaches. This work has great applications to both public and private sectors that share information to the society.
목차
Abstract
1. Introduction
2. Related Work
2.1. Data Anonymization Using One-Dimensional Mapreduce Framework
2.2. Datafly Algorithm for the Data Anonymization
2.3. Mondrian Algorithms for the Data Anonymization
3. Methodology
3.1. Sketch of Two-Phase Top-Down Specialization
3.2 Data Partition
3.3 Anonymization Level Merging
3.4 Data Specialization
3.5. Mapreduce with Multidimensional Anonymization
4. Results and Evaluations
5. Conclusion
6. Future Work
References
키워드
Data Anonymization Big Data Cloud Computing MapReduce Privacy Preservation Top Down Specialization저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Database Theory and Application
- 간기
- 격월간
- pISSN
- 2005-4270
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.8 No.6
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