C4.5: programs for machine learning
C4.5: programs for machine learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the forty-first annual ACM symposium on Theory of computing
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th 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
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
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
Mixture of gaussian models and bayes error under differential privacy
Proceedings of the first ACM conference on Data and application security and privacy
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Differentially private data cubes: optimizing noise sources and consistency
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
iReduct: differential privacy with reduced relative errors
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Personal privacy vs population privacy: learning to attack anonymization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Cloning for privacy protection in multiple independent data publications
Proceedings of the 20th ACM international conference on Information and knowledge management
Differential privacy for location pattern mining
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
A Practical Differentially Private Random Decision Tree Classifier
Transactions on Data Privacy
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
The application of differential privacy to health data
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PrivBasis: frequent itemset mining with differential privacy
Proceedings of the VLDB Endowment
Low-rank mechanism: optimizing batch queries under differential privacy
Proceedings of the VLDB Endowment
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Differentially private top-k query over MapReduce
Proceedings of the fourth international workshop on Cloud data management
Worst- and average-case privacy breaches in randomization mechanisms
TCS'12 Proceedings of the 7th IFIP TC 1/WG 202 international conference on Theoretical Computer Science
Differential privacy data release through adding noise on average value
NSS'12 Proceedings of the 6th international conference on Network and System Security
Differential private trajectory protection of moving objects
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Anonymizing classification data using rough set theory
Knowledge-Based Systems
On Learning Cluster Coefficient of Private Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
PrivGene: differentially private model fitting using genetic algorithms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
DiffR-Tree: a differentially private spatial index for OLAP query
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard
Computer Methods and Programs in Biomedicine
Understanding hierarchical methods for differentially private histograms
Proceedings of the VLDB Endowment
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
Differentially private histogram publication
The VLDB Journal — The International Journal on Very Large Data Bases
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We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, as we show in the paper, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples.