Data Mining and Knowledge Discovery
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
A novel data distortion approach via selective SSVD for privacy protection
International Journal of Information and Computer Security
Privacy Preserving Data Mining Research: Current Status and Key Issues
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Data Randomization for Lightweight Secure Data Aggregation in Sensor Network
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Data Base
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Towards the evaluation of time series protection methods
Information Sciences: an International Journal
International Journal of Data Analysis Techniques and Strategies
K-anonymous association rule hiding
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Surveillance, persuasion, and panopticon
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Information driven evaluation of data hiding algorithms
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
“Secure” log-linear and logistic regression analysis of distributed databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Forecasting using rules extracted from privacy preservation neural network
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Trends and research directions for privacy preserving approaches on the cloud
Proceedings of the 6th ACM India Computing Convention
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Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed.