Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Incremental Maintenance of Online Summaries Over Multiple Streams
IEEE Transactions on Knowledge and Data Engineering
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Privacy Preserving k-Anonymity for Re-publication of Incremental Datasets
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
An Incremental Updating Algorithm for Online Mining Association Rules
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
Privacy-Preserving Gradient-Descent Methods
IEEE Transactions on Knowledge and Data Engineering
Effective Reconstruction of Data Perturbed by Random Projections
IEEE Transactions on Computers
Privacy Preserving Decision Tree Learning Using Unrealized Data Sets
IEEE Transactions on Knowledge and Data Engineering
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There has been a wide area of research going on towards privacy preservation of data. This method, Integer Partitioning Based Encryption (IPBE) does not use a simple perturbation or straight forward public key encryption. The method groups the data into various classes and the encryption is based on the key values generated within each class. Since the key is not a constant private or public key, the method provides a greater amount of protection compared to usual cryptographic techniques. In this paper, the algorithm is specified with a sample input and output database. The method does not require the execution of entire database after insertion. The usual disadvantages of other privacy protection algorithms are overcome in this methodology and are explained with examples. The performance of the methodology is also expressed using a graph.