Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Disclosure Risk Assessment in Perturbative Microdata Protection
Inference Control in Statistical Databases, From Theory to Practice
Exact and approximate methods for data directed microaggregation in one or more dimensions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information Fusion in Data Mining
Information Fusion in Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata
Data Mining and Knowledge Discovery
A Framework for Evaluating Privacy Preserving Data Mining Algorithms*
Data Mining and Knowledge Discovery
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Two methods for privacy preserving data mining with malicious participants
Information Sciences: an International Journal
Rethinking rank swapping to decrease disclosure risk
Data & Knowledge Engineering
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Data Access in a Cyber World: Making Use of Cyberinfrastructure
Transactions on Data Privacy
A Measure of Disclosure Risk for Tables of Counts
Transactions on Data Privacy
Clustering of time series data-a survey
Pattern Recognition
Extending microaggregation procedures for time series protection
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Distance based re-identification for time series, analysis of distances
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Information Sciences: an International Journal
A review on time series data mining
Engineering Applications of Artificial Intelligence
Discord region based analysis to improve data utility of privately published time series
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
SMART: Stream Monitoring enterprise Activities by RFID Tags
Information Sciences: an International Journal
Information fusion in data privacy: A survey
Information Fusion
Variable linkage for multimedia metadata schema matching
Multimedia Tools and Applications
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The goal of statistical disclosure control (SDC) is to modify statistical data so that it can be published without releasing confidential information that may be linked to specific respondents. The challenge for SDC is to achieve this variation with minimum loss of the detail and accuracy sought by final users. There are many approaches to evaluate the quality of a protection method. However, all these measures are only applicable to numerical or categorical attributes. In this paper, we present some recent results about time series protection and re-identification. We propose a complete framework to evaluate time series protection methods. We also present some empirical results to show how our framework works.