Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
SELinux: NSA's Open Source Security Enhanced Linux
SELinux: NSA's Open Source Security Enhanced Linux
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering frequent patterns in sensitive data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
Can the Utility of Anonymized Data be Used for Privacy Breaches?
ACM Transactions on Knowledge Discovery from Data (TKDD)
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Differentially Private Spatial Decompositions
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
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Discovering that Map-Reduce framework is a popular way to deal with a large scale of data, but there is a significant risk to leak out users' personal information, especially when the data is sensitive, for example, including users' health records, salary information, etc. Differential privacy has recently emerged as a new paradigm for preserving private data. This makes it possible to provide strong theoretical guarantees on the privacy and utility of the query results. In this paper, we focus on top-k query which is one of the most useful queries in Map-Reduce framework over big data sets. Motivated by this, we propose an efficient algorithm, called DiffMR Differentially private Top-kquery over MapReduce), for processing top-k query as well as satisfying differential privacy. In our algorithm, to avoid the private leak in middle process, we use exponential mechanism to select top-k records from big data sets by using score function. When the data set is too large to get a reasonably accurate result, we can reduce the reject rate and execute several more times Map-Reduce to get a more accurate top-k query result. After getting a final top-k candidate result, we will add Laplace noise to each record and adopt post-processing technique to improve the accuracy of query answers. Our experimental study demonstrates that DiffMR algorithm can be used to answer the top-k query accurately in Map-Reduce framework.