LiWu Chang

Privacy-Preserving collaborative association rule mining

This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among parties involved in a data mining task. We study how to share private or confidential data in the following scenario: multiple parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for multiple parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private.

Privacy-Preserving Naive Bayesian Classification over Horizontally Partitioned Data

Protection of privacy is a critical problem in data mining. Preserving data privacy in distributed data mining is even more challenging. In this paper, we consider the problem of privacy-preserving naive Bayesian classiflcation over vertically partitioned data. The problem is one of important issues in privacy- preserving distributed data mining. Our approach is based on homomorphic encryption. The scheme is very e-cient in the term of computation and communication cost.

Private Mining of Association Rules

This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parties involved in a data mining task. We study how to share private or confidential data in the following scenario: two parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for two parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning the association rules.

Building k-nearest neighbor classifiers on vertically partitioned private data

This paper considers how to conduct k-nearest neighbor classification in the following scenario: multiple parties, each having a private data set, want to collaboratively build a k-nearest neighbor classifier without disclosing their private data to each other or any other parties. Specifically, the data are vertically partitioned in that all parties have data about all the instances involved, but each party has its own view of the instances - each party works with its own attribute set. Because of privacy constraints, developing a secure framework to achieve such a computation is both challenging and desirable. In this paper, we develop a secure protocol for multiple parties to conduct the desired computation. All the parties participate in the encryption and in the computation involved in learning the k-nearest neighbor classifiers.

Privacy-preserving multi-party decision tree induction

Data mining is a process to extract useful knowledge from large amounts of data. To conduct data mining, we often need to collect data. However, sometimes the data are distributed among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties can collaboratively conduct data mining without breaching data privacy presents a grand challenge. In this paper, we propose a randomisation-based scheme for multi-parties to conduct data mining computations without disclosing their actual data sets to each other.