Justin Zhijun Zhan

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Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specially, we present a method to build decision tree classifiers from the disguised data. We conduct experiments to compare the accuracy of our decision tree with the one built(More)
Secure Multi-party Computation (SMC) problems deal with the following situation: Two (or many) parties want to jointly perform a computation. Each party needs to contribute its private input to this computation, but no party should disclose its private inputs to the other parties, or to any third party. With the proliferation of the Internet, SMC problems(More)
In recent times, the development of privacy technologies has promoted the speed of research on privacy-preserving collaborative data mining. People borrowed the ideas of secure multi-party computation and developed secure multi-party protocols to deal with privacy-preserving collaborative data mining problems. Random perturbation was also identified to be(More)
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the(More)
To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. In this paper, we propose a formal definition of(More)
While privacy preservation of data mining approaches has been an important topic for a number of years, privacy of social network data is a relatively new area of interest. Previous research has shown that anonymization alone may not be sufficient for hiding identity information on certain real world data sets. In this paper, we focus on understanding the(More)
Trust is a phenomenon that is exclusively possessed by human beings. Due to its human-related properties, trust is difficult to be uniformly defined or even be precisely described. As a research field, trust has been intensively focused on exploring propagations and usefulness in social networks. Little research work has been found on simulating trust(More)
Phishing attack has been a serious concern to online banking and e-commerce websites. This paper proposes a method to detect and filter phishing emails in dynamic environment by applying a family of weak estimators. Anomaly detection identifies observations that deviate from the normal behavior of a system and is achieved by identifying the phenomena that(More)
Data collection is a necessary step in data mining process. Due to privacy reasons, collecting data from different parties becomes difficult. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a(More)