Privacy preserving similarity joins using MapReduce
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Hai Jin | Kim-Kwang Raymond Choo | Xiaofeng Ding | Xiaoli Wang | Wanlu Yang | Hai Jin | Xiaofeng Ding | Xiaoli Wang | Wanlu Yang
[1] Latanya Sweeney,et al. k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[2] Vitaly Shmatikov,et al. Airavat: Security and Privacy for MapReduce , 2010, NSDI.
[3] Andreas Haeberlen,et al. DJoin: differentially private join queries over distributed databases , 2012, OSDI 2012.
[4] Anthony K. H. Tung,et al. Efficient and Scalable Processing of String Similarity Join , 2013, IEEE Transactions on Knowledge and Data Engineering.
[5] Keke Gai,et al. Privacy-Preserving Content-Oriented Wireless Communication in Internet-of-Things , 2018, IEEE Internet of Things Journal.
[6] Zhao Zhang,et al. Supporting user authorization queries in RBAC systems by role-permission reassignment , 2018, Future Gener. Comput. Syst..
[7] Ulf Leser,et al. Set Similarity Joins on MapReduce: An Experimental Survey , 2018, Proc. VLDB Endow..
[8] Bhaskar DasGupta,et al. On analyzing and evaluating privacy measures for social networks under active attack , 2018, Inf. Sci..
[9] Surajit Chaudhuri,et al. A Primitive Operator for Similarity Joins in Data Cleaning , 2006, 22nd International Conference on Data Engineering (ICDE'06).
[10] Guoliang Li,et al. Can we beat the prefix filtering?: an adaptive framework for similarity join and search , 2012, SIGMOD Conference.
[11] Emiliano De Cristofaro,et al. (If) Size Matters: Size-Hiding Private Set Intersection , 2011, IACR Cryptol. ePrint Arch..
[12] Hai Jin,et al. Privacy-Preserving Multi-Keyword Top-$k$ k Similarity Search Over Encrypted Data , 2019, IEEE Trans. Dependable Secur. Comput..
[13] Vassilios S. Verykios,et al. An LSH-Based Blocking Approach with a Homomorphic Matching Technique for Privacy-Preserving Record Linkage , 2015, IEEE Transactions on Knowledge and Data Engineering.
[14] Guoliang Li,et al. MassJoin: A mapreduce-based method for scalable string similarity joins , 2014, 2014 IEEE 30th International Conference on Data Engineering.
[15] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[16] Shouling Ji,et al. Structural Data De-Anonymization: Theory and Practice , 2016, IEEE/ACM Transactions on Networking.
[17] Kim-Kwang Raymond Choo,et al. Enabling verifiable multiple keywords search over encrypted cloud data , 2018, Inf. Sci..
[18] Ninghui Li,et al. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[19] Guoliang Li,et al. K-Join: Knowledge-Aware Similarity Join , 2016, IEEE Trans. Knowl. Data Eng..
[20] Benny Pinkas,et al. Scalable Private Set Intersection Based on OT Extension , 2018, IACR Cryptol. ePrint Arch..
[21] Fenghua Li,et al. Server-aided private set intersection based on reputation , 2017, Inf. Sci..
[22] Weidong Xiao,et al. Fast top-k similarity join for SimRank , 2017, Inf. Sci..
[23] Johannes Gehrke,et al. iReduct: differential privacy with reduced relative errors , 2011, SIGMOD '11.
[24] ASHWIN MACHANAVAJJHALA,et al. L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).
[25] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[26] Raghav Kaushik,et al. Efficient exact set-similarity joins , 2006, VLDB.
[27] Yan Li,et al. A distributed ensemble approach for mining healthcare data under privacy constraints , 2016, Inf. Sci..
[28] Yi Liu,et al. Secure multi-label data classification in cloud by additionally homomorphic encryption , 2018, Inf. Sci..
[29] Hongwei Li,et al. Secure Multi-Party Computation: Theory, practice and applications , 2019, Inf. Sci..
[30] Jeffrey Xu Yu,et al. Efficient similarity joins for near-duplicate detection , 2011, TODS.
[31] Xike Xie,et al. Embedding differential privacy in decision tree algorithm with different depths , 2016, Science China Information Sciences.
[32] C. L. Philip Chen,et al. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..
[33] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[34] Gang Chen,et al. Metric Similarity Joins Using MapReduce , 2017, IEEE Transactions on Knowledge and Data Engineering.
[35] Beng Chin Ooi,et al. M2R: Enabling Stronger Privacy in MapReduce Computation , 2015, USENIX Security Symposium.
[36] John Riedl,et al. You are what you say: privacy risks of public mentions , 2006, SIGIR '06.
[37] Chen Li,et al. Efficient parallel set-similarity joins using MapReduce , 2010, SIGMOD Conference.
[38] Cong Wang,et al. Privacy-Preserving Similarity Joins Over Encrypted Data , 2017, IEEE Transactions on Information Forensics and Security.
[39] Shiho Moriai,et al. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption , 2018, IEEE Transactions on Information Forensics and Security.
[40] Ray R. Larson. Introduction to Information Retrieval , 2010 .
[41] Josep Domingo-Ferrer,et al. Big Data Privacy: Challenges to Privacy Principles and Models , 2015, Data Science and Engineering.
[42] Keke Gai,et al. Privacy-Preserving Energy Trading Using Consortium Blockchain in Smart Grid , 2019, IEEE Transactions on Industrial Informatics.
[43] Keke Gai,et al. Blend Arithmetic Operations on Tensor-Based Fully Homomorphic Encryption Over Real Numbers , 2018, IEEE Transactions on Industrial Informatics.