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IEEE Data Mining Projects
KNOWLEDGE AND DATA ENGINEERING
PROTECTION OF DATABASE SECURITY VIA COLLABORATIVE INFERENCE DETECTION:--J2EE--2008
"Malicious users can exploit the correlation among data to infer sensitive information from a series of seemingly innocuous data accesses. Thus, we develop an inference violation detection system to protect sensitive data content. Based on data dependency, database schema and semantic knowledge. we constructed a semantic inference model (SIM) that represents the possible inference channels from any attribute to the pre-assigned sensitive attributes. The SIM is then instantiated to a semantic inference graph (SIG) for query-time inference violation detection. For a single user case, when a user poses a query, the detection system will examine his/her past query log and calculate the probability of inferring sensitive information. The query request will be denied if the inference probability exceeds the pre specified threshold. For multi-user cases, the users may share their query answers to increase the inference probability. Therefore, we develop a model to evaluate collaborative inference based on the query sequences of collaborators and their task-sensitive collaboration levels. Experimental studies reveal that information authoritativeness, communication fidelity and honesty in collaboration are three key factors that affect the level of achievable collaboration. An example is given to illustrate the use of the proposed technique to prevent multiple collaborative users from deriving sensitive information via inference.
HARDWARE ENHANCED ASSOCIATION RULE MINING WITH HASHING AND PIPELINING:--DOTNET--2008
Data mining techniques have been widely used in various applications. One of the most important data mining applications is association rule mining. Apriori-based association rule mining in hardware, one has to load candidate item sets and a database into the hardware. Since the capacity of the hardware architecture is fixed, if the number of candidate item sets or the number of items in the database is larger than the hardware capacity, the items are loaded into the hardware separately. The time complexity of those steps that need to load candidate item sets or database items into the hardware is in proportion to the number of candidate item sets multiplied by the number of items in the database. Too many candidate item sets and a large database would create a performance bottleneck. In this paper, we propose a HAsh-based and PiPelIned (abbreviated as HAPPI) architecture for hardware enhanced association rule mining. Therefore, we can effectively reduce the frequency of loading the database into the hardware. HAPPI solves the bottleneck problem in a priori-based hardware schemes.
WATERMARKING RELATIONAL DATABASES USING OPTIMIZATION-BASED TECHNIQUES:--DOTNET--2008
Proving ownerships rights on outsourced relational database is a crucial issue in today's internet based application environments and in many content distribution applications. In this paper, we present a mechanism for proof of ownership based on the secure embedding of a robust imperceptible watermark in relational data. We formulate the watermarking of relational databases as a constrained optimization problem and discus efficient techniques to solve the optimization problem and to handle the onstraints. Our watermarking technique is resilient to watermark synchronization errors because it uses a partioning approach that does not require marker tuple. Our approach overcomes a major weakness in previously proposed watermarking techniques. Watermark decoding is based on a threshold-based technique characterized by an optimal threshold that minimizes the probability of decoding errors. We implemented a proof of concept implementation of our watermarking technique and showed by experimental results that our technique is resilient to tuple deletion, alteration and insertion attacks
TRUTH DISCOVERY WITH MULTIPLE CONFLICTING INFORMATION PROVIDERS ON THE WEB:--J2EE-2008
The World Wide Web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the Web. Moreover, different websites often provide conflicting information on a subject, such as different specifications for the same product. In this paper, we propose a new problem, called Veracity, i.e., conformity to truth, which studies how to find true facts from a large amount of conflicting information on many subjects that is provided by various websites. We design a general framework for the Veracity problem and invent an algorithm, called TRUTHFINDER, which utilizes the relationships between websites and their information, i.e., a website is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy websites. An iterative method is used to infer the trustworthiness of websites and the correctness of information from each other. Our experiments show that TRUTHFINDER successfully finds true facts among conflicting information and identifies trustworthy websites better than the popular search engines.
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