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» Privacy Preserving Clustering by Data Transformation
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TDP
2010
140views more  TDP 2010»
13 years 2 months ago
Movement Data Anonymity through Generalization
In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diffusion of mobile devices (e.g., mobile phones, RFID devices and GPS ...
Anna Monreale, Gennady L. Andrienko, Natalia V. An...
ADC
2007
Springer
145views Database» more  ADC 2007»
14 years 1 months ago
The Privacy of k-NN Retrieval for Horizontal Partitioned Data -- New Methods and Applications
Recently, privacy issues have become important in clustering analysis, especially when data is horizontally partitioned over several parties. Associative queries are the core retr...
Artak Amirbekyan, Vladimir Estivill-Castro
ICDE
2005
IEEE
137views Database» more  ICDE 2005»
14 years 9 months ago
Extending Relational Database Systems to Automatically Enforce Privacy Policies
Databases are at the core of successful businesses. Due to the voluminous stores of personal data being held by companies today, preserving privacy has become a crucial requiremen...
Rakesh Agrawal, Paul Bird, Tyrone Grandison, Jerry...
INFOVIS
2003
IEEE
14 years 26 days ago
Visualization of Labeled Data Using Linear Transformations
We present a novel family of data-driven linear transformations, aimed at visualizing multivariate data in a low-dimensional space in a way that optimally preserves the structure ...
Yehuda Koren, Liran Carmel
ICDE
2007
IEEE
165views Database» more  ICDE 2007»
14 years 9 months ago
On Randomization, Public Information and the Curse of Dimensionality
A key method for privacy preserving data mining is that of randomization. Unlike k-anonymity, this technique does not include public information in the underlying assumptions. In ...
Charu C. Aggarwal