28848292
Rights Protection for Data Cubes
We propose a rights protection scheme for data cubes. The scheme embeds ownership information by modifying a set of selected cell values. The embedded message will not affect the usefulness of data cubes in the sense that the sum queries at any aggregation level are not affected. At the same time, the errors introduced to individual cell values are under control. The embedded message can be detected with a high probability even in the presence of typical data cube attacks. The proposed scheme can thus be used for protecting data cubes from piracy in an open, distributed environment.
18397167
New Paradigm of Inference Control with Trusted Computing
The database server is a crucial bottleneck in traditional inference control architecture, as it enforces highly computation-intensive auditing for all users who query the protected database. As a result, most auditing methods, though rigorously studied, can never be implemented in practice for protecting largescale real-world database systems. To shift this paradigm, we propose a new inference control architecture that will entrust inference control to each users platform, provided that the platform is equipped with trusted computing technology. The trusted computing technology is designed to attest the state of a users platform to the database server, so as to assure the server that inference control could be enforced as expected. A generic protocol is proposed to formalize the interactions between the users platform and database server. Any existing inference control technique can work with our protocol, for which the security properties are formally proven. Since each user's platform enforces inference control for its own queries, our solution avoids the bottleneck.
36131731
Disclosure Risk in Dynamic Two-Dimensional Contingency Tables (Extended Abstract)
Two-dimensional contingency tables are central products of many information organizations such as statistical agencies, census bureaus, and health insurance information agencies. In a contingency table, any sensitive information about individuals must be protected, while some aggregated information can be released. The disclosure risk is that the aggregated information can be used to infer some sensitive information about individuals. Since the disclosure of sensitive information may compromise privacy, confidentiality, and national interests, one needs to carefully assess the latent risk of disclosure and take effective methods to protect the data.
26456568
Disclosure Analysis for Two-Way Contingency Tables
Disclosure analysis in two-way contingency tables is important in categorical data analysis. The disclosure analysis concerns whether a data snooper can infer any protected cell values, which contain privacy sensitive information, from available marginal totals (i.e., row sums and column sums) in a two-way contingency table. Previous research has been targeted on this problem from various perspectives. However, there is a lack of systematic definitions on the disclosure of cell values. Also, no previous study has been focused on the distribution of the cells that are subject to various types of disclosure. In this paper, we define four types of possible disclosure based on the exact upper bound and/or the lower bound of each cell that can be computed from the marginal totals. For each type of disclosure, we discover the distribution pattern of the cells subject to disclosure. Based on the distribution patterns discovered, we can speed up the search for all cells subject to disclosure.
14671442
An Approach to Outsourcing Data Mining Tasks while Protecting Business Intelligence and Customer Privacy
Data mining is playing an important role in decision making. It is beneficial to outsource data mining tasks if an organization does not have required expertise in-house. However, the organization may lose business intelligence and customer privacy during this outsourcing process. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules while protecting their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off storage requirements
17592729
On the Lower Bound of Reconstruction Error for Spectral Filtering Based Privacy Preserving Data Mining
Additive Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on Spectral Filtering empirically showed that individual data can be separated from the perturbed one and as a result privacy can be seriously compromised. Our previous work initiated the theoretical study on how the estimation error varies with the noise and gave an upper bound for the Frobenius norm of reconstruction error using matrix perturbation theory. In this paper, we propose one Singular Value Decomposition (SVD) based reconstruction method and derive a lower bound for the reconstruction error. We then prove the equivalence between the Spectral Filtering based approach and the proposed SVD approach and as a result the achieved lower bound can also be considered as the lower bound of the Spectral Filtering based approach.
1111436
Adaptive resource management in active network nodes
Resource management in active network nodes is challenging because more types and amount of resources are needed compared with traditional networks, and the consumption of different types of resources is not equally balanced. Hence, it is possible and important to consider the relationship between different types of resources, needed by an application. Adaptation of applications resource usages in network nodes enables the applications to react promptly to the resource status in the network nodes and therefore offers a better performance guarantee and more flexibility to the applications. This paper studies the resource management in active network nodes considering the relationship among different types of resources. The resource vectors (RVs) are introduced to describe the resource requirements of applications and their adaptation capabilities. And an adaptive resource management mechanism based on RV is proposed and the implementation of it in an active node is given.
3052468
Performance Modeling and Analysis for Resource Scheduling in Data Grids
Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.
8278906
A Hybrid Method of Unsupervised Feature Selection Based on Ranking
Feature selection is a key problem to pattern recognition. So far, most methods of feature selection focus on sample data where class information is available. For sample data without class labels, however, the related methods for feature selection are few. This paper proposes a new way of unsupervised feature selection. Our method is a hybrid approach based on ranking the features according to their relevance to clustering using a new ranking index which belongs to exponential entropy. Firstly a candidate feature subset is selected using a modified fuzzy feature evaluation index (FFEI) with a new method to calculate the feature weight, which makes the algorithm to be robust and independent of domain knowledge. Then a wrapper method is used to select compact feature subset from the candidate feature set based on the clustering performance. Experimental results on benchmark data sets indicate the effectiveness of the proposed method
11166931
Automatic Extraction of Hierarchical Relations from Text
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.
25738322
GABI-Kat SimpleSearch: A Flanking Sequence Tag (FST) Database for the Identification of T-DNA Insertion Mutants in Arabidopsis Thaliana
SUMMARY
GABI-Kat SimpleSearch is a database of flanking sequence tags (FSTs) of T-DNA mutagenized Arabidopsis thaliana lines that were generated by the GABI-Kat project. Sequences flanking the T-DNA insertion sites were aligned to the A.thaliana genome sequence, annotated with information about the FST, the insertion site and the line from which the FST was derived. A web interface permits text-based as well as sequence-based searches for relevant insertions. GABI-Kat SimpleSearch aims to help biologists to quickly find T-DNA insertion mutants for their research.


AVAILABILITY
http://www.mpiz-koeln.mpg.de/GABI-Kat/