A MapReduce Based Approach of Scalable Multidimensional Anonymization for Big Data Privacy Preservation on Cloud
Abstract
The massive increase in computing power and data storage capacity provisioned by cloud computing as well as advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals by orders of magnitude. Meanwhile, privacy protection is one of most concerned issues in big data and cloud applications, thereby requiring strong preservation of customer privacy and attracting considerable attention from both IT industry and academia. Data anonymization provides an effective way for data privacy preservation, and multidimensional anonymization scheme is a widely-adopted one among existing anonymization schemes. However, existing multidimensional anonymization approaches suffer from severe scalability or IT cost issues when handling big data due to their incapability of fully leveraging cloud resources or being cost-effectively adapted to cloud environments. As such, we propose a scalable multidimensional anonymization approach for big data privacy preservation using Map Reduce on cloud. In the approach, a highly scalable median-finding algorithm combining the idea of the median of medians and histogram technique is proposed and the recursion granularity is controlled to achieve cost-effectiveness. Corresponding MapReduce jobs are dedicatedly designed, and the experiment evaluations demonstrate that with our approach, the scalability and cost-effectiveness of multidimensional scheme can be improved significantly over existing approaches.
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In traditional emergency access systems, the patient cannot give consent to emergency staff to access their PHR.
“In a traditional emergency access system, the patient cannot give consent to emergency staff for accessing his/her PHR.”
From EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain (2019) · cites this paper as [15]
Existing studies in emergency access for PHR without blockchain are the traditional system, and some studies based on blockchain do not address access to PHR in an emergency condition.
“These existing studies in emergency access for PHR without blockchain are the traditional system, and also there are some studies based on blockchain but not access to PHR in an emergency condition.”
From EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain (2019) · cites this paper as [15]
Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · see citing works
<i>L</i> -diversity: Privacy beyond <i>k</i> -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617 · see citing works
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802 · see citing works
Privacy-preserving data publishing: A survey of recent developments
Fung, B. C. M., Wang, K., Chen, R., & Yu, P. S. (2010) · ACM Computing Surveys · doi:10.1145/1749603.1749605 · Cited by 1,704 · cited 4× · see citing works
“Multidimensional anonymization scheme is defined by a single function [3, 4], i.”
BackgroundInfluentialBlum, M., Floyd, R. W., Pratt, V., et al. (1973) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(73)80033-9 · Cited by 1,255 · see citing works
Dean, J., & Ghemawat, S. (2009) · Communications of the ACM · doi:10.1145/1629175.1629198 · Cited by 1,185 · see citing works
Mondrian Multidimensional K-Anonymity
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2006) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde.2006.101 · Cited by 1,134 · see citing works
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2005) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1066157.1066164 · Cited by 1,073 · see citing works
The new Casper: query processing for location services without compromising privacy
Mokbel, M. F., Chow, C.-Y., & Aref, W. G. (2006) · doi:10.5555/1182635.1164193 · Cited by 1,004 · see citing works
Personalized privacy preservation
Xiao, X., & Tao, Y. (2006) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1142473.1142500 · Cited by 674 · see citing works
Anatomy: simple and effective privacy preservation
Xiao, X., & Tao, Y. (2006) · Very Large Data Bases · doi:10.5555/1182635.1164141 · Cited by 657 · see citing works
Parallel data processing with MapReduce
Lee, K.-H., Lee, Y.-J., Choi, H., et al. (2012) · ACM SIGMOD Record · doi:10.1145/2094114.2094118 · Cited by 562 · see citing works
Xiao, X., & Tao, Y. (2007) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1247480.1247556 · Cited by 463 · see citing works
Airavat: security and privacy for MapReduce
Roy, I., Setty, S., Kilzer, A., Shmatikov, V., & Witchel, E. (2010) · doi:10.5555/1855711.1855731 · Cited by 417 · see citing works
Anonymizing Classification Data for Privacy Preservation
Fung, B. C. M., Wang, K., & Yu, P. S. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.1015 · Cited by 351 · see citing works
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Gehrke, J. E., Ramakrishnan, R., & Ganti, V. (2000) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009839829793 · Cited by 347 · see citing works
Slicing: A New Approach for Privacy Preserving Data Publishing
Li, T., Li, N., Zhang, J., & Molloy, I. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.236 · Cited by 312 · see citing works
Zhang, X., Yang, L. T., Liu, C., et al. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.48 · Cited by 241 · see citing works
Self-citationCloseness: A New Privacy Measure for Data Publishing
Li, N., Li, T., & Venkatasubramanian, S. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.139 · Cited by 204 · see citing works
The Sampling Distribution of the Coefficient of Variation
Hendricks, W. A., & Robey, K. W. (1936) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732503 · Cited by 181 · see citing works
Zhang, K., Zhou, X., Chen, Y., et al. (2011) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/2046707.2046767 · Cited by 160 · see citing works
Zhang, X., Liu, C., Nepal, ., Pandey, S., & Chen, J. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.238 · Cited by 132 · see citing works
Self-citationBorkar, V., Carey, M. J., & Li, C. (2012) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/2247596.2247598 · Cited by 132 · see citing works
Handicapping attacker's confidence: an alternative to k-anonymization
Wang, K., Fung, B. C. M., & Yu, P. S. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0035-5 · Cited by 121 · see citing works
Workload-aware anonymization techniques for large-scale datasets
LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2008) · ACM Transactions on Database Systems · doi:10.1145/1386118.1386123 · Cited by 119 · see citing works
Chaudhuri, S. (2012) · ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS) · doi:10.1145/2213556.2213558 · Cited by 104 · see citing works
Scalable and Parallel Boosting with MapReduce
Palit, I., & Reddy, C. K. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.208 · Cited by 96 · see citing works
Maintaining K-Anonymity against Incremental Updates
Pei, J., Xu, J., Wang, Z., et al. (2007) · International Conference on Scientific and Statistical Database Management · doi:10.1109/ssdbm.2007.16 · Cited by 94 · see citing works
K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization
Iwuchukwu, T., DeWitt, D. J., Doan, A., & Naughton, J. F. (2007) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2007.369024 · Cited by 89 · see citing works
Privacy preservation by disassociation
Terrovitis, M., Mamoulis, N., Liagouris, J., et al. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2336664.2336668 · Cited by 85 · see citing works
Puttaswamy, K. P. N., Kruegel, C., & Zhao, B. Y. (2011) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2038916.2038926 · Cited by 83 · see citing works
Utility-based anonymization for privacy preservation with less information loss
Xu, J., Wang, W., Pei, J., et al. (2006) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1233321.1233324 · Cited by 77 · see citing works
The HybrEx model for confidentiality and privacy in cloud computing
Ko, S. Y., Jeon, K., & Morales, R. (2011) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.5555/2170444.2170452 · Cited by 68 · see citing works
PRISM – Privacy-Preserving Search in MapReduce
Blaß, E.-O., Pietro, R. D., Molva, R. A., & Önen, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31680-7_10 · Cited by 61 · see citing works
Zhang, X., Liu, C., Nepal, ., Yang, C., Dou, W., & Chen, J. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.235 · Cited by 32 · see citing works
Self-citationSlicing: A New Approach to Privacy Preserving Data PublishingPreprint
Li, T., Li, N., Zhang, J., et al. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.2290 · Cited by 8 · see citing works
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EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain
Rajput, A. R., Li, Q., Taleby Ahvanooey, M., & Masood, I. (2019) · IEEE Access · doi:10.1109/access.2019.2917976 · Cited by 166 · see citing works
- Authors
- Zhang, X., Yang, C., Nepal, S., Liu, C., Dou, W., & Chen, J.
- Affiliations
- University of Technology Sydney (AU)
- State Key Laboratory of New Technology of Computer Software (CN)
- Venue
- 2013 International Conference on Cloud and Green Computing
- Publisher
- IEEE
- Published
- 2013-09
- Pages
- 105-112
- OpenAlex
- W1998654583
- Cited by
- 43
- References
- 36
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 2 · 2019: 7 · 2018: 2 · 2017: 10 · 2016: 3 · 2015: 7
- FWCI
- 2.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Cloud and Green Computing (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- data anonymization, big data privacy, cloud computing, MapReduce, scalability, cost-effectiveness, privacy preservation
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