11271251
Top-down specialization for information and privacy preservation
Releasing person-specific data in its most specific state poses a threat to individual privacy. This paper presents a practical and efficient algorithm for determining a generalized version of data that masks sensitive information and remains useful for modelling classification. The generalization of data is implemented by specializing or detailing the level of information in a top-down manner until a minimum privacy requirement is violated. This top-down specialization is natural and efficient for handling both categorical and continuous attributes. Our approach exploits the fact that data usually contains redundant structures for classification. While generalization may eliminate some structures, other structures emerge to help. Our results show that quality of classification can be preserved even for highly restrictive privacy requirements. This work has great applicability to both public and private sectors that share information for mutual benefits and productivity.
2174685
Privacy-enhancing personalized web search
Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.
437107
Template-based privacy preservation in classification problems
In this paper, we present a template-based privacy preservation to protect against the threats caused by data mining abilities. The problem has dual goals: preserve the information for a wanted classification analysis and limit the usefulness of unwanted sensitive inferences that may be derived from the data. Sensitive inferences are specified by a set of "privacy templates". Each template specifies the sensitive information to be protected, a set of identifying attributes, and the maximum association between the two. We show that suppressing the domain values is an effective way to eliminate sensitive inferences. For a large data set, finding an optimal suppression is hard, since it requires optimization over all suppressions. We present an approximate but scalable solution. We demonstrate the effectiveness of this approach on real life data sets.
1098870
Hierarchical Document Clustering using Frequent Itemsets
A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, each document often contains a small fraction of words in the vocabulary. These features require special handlings. Another requirement is hierarchical clustering where clustered documents can be browsed according to the increasing specificity of topics. In this paper, we propose to use the notion of frequent itemsets, which comes from association rule mining, for document clustering. The intuition of our clustering criterion is that each cluster is identified by some common words, called frequent itemsets, for the documents in the cluster. Frequent itemsets are also used to produce a hierarchical topic tree for clusters. By focusing on frequent items, the dimensionality of the document set is drastically reduced. We show that this method outperforms best existing methods in terms of both clustering accuracy and scalability.
304685
Anonymizing sequential releases
An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.
46395996
Coordinated Resource Allocation by Mutual Outsourcing in Decentralized Supply Chain
Lumpy demand forces capacity planners to maximize the profit of individual factories as well as simultaneously take advantage of outsourcing from its supply chain and even competitors. This study examines a capacity planning business model in which consists of many profit-centered factories (autonomous agents). We propose an ant algorithm to solve a set of non-linear mixed integer programming models with different economic objectives and constraints. The proposed method allows a mutually acceptable capacity plan for a set of customer tasks to be allocated by the negotiating parties, each with information on company objectives, cost and price. Experiment results reveal that near optimal solutions for both isolated (a single factory) and negotiation-based (between factories) environments are obtained.
384033
Forgetting in Managing Rules and Ontologies
The language of HEX-programs under the answer-set semantics is designed for interoperating with heterogeneous sources via external atoms and for meta-reasoning via higher-order literals in the context of the semantic Web. As an important technique in managing knowledge bases, the notion of forgetting has received increasing interest in the knowledge-representation area. In this paper, we introduce a semantics-based theory of forgetting for HEX-programs and, in turn, for a class of OWL/RDF(S) ontologies which allows to fully employ semantic information in managing ontologies like editing, merging, aligning, and redundancy removal
4537262
A semantic framework for preference handling in answer set programming
We provide a semantic framework for preference handling in answer set programming. To this end, we introduce preference preserving consequence operators. The resulting fixpoint characterizations provide us with a uniform semantic framework for characterizing preference handling in existing approaches. Although our approach is extensible to other semantics by means of an alternating fixpoint theory, we focus here on the elaboration of preferences under answer set semantics. Alternatively, we show how these approaches can be characterized by the concept of order preservation. These uniform semantic characterizations provide us with new insights about inter-relationships and moreover about ways of implementation.
52857898
Schema Discovery for Semistructured Data
To formulate a meaningful query on semistructured data, such as on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies.
30385305
Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes
Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. 
 
This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.