6924123
Publicly verifiable ownership protection for relational databases
Today, watermarking techniques have been extended from the multimedia context to relational databases so as to protect the ownership of data even after the data are published or distributed. However, all existing watermarking schemes for relational databases are secret key based, thus require a secret key to be presented in proof of ownership. This means that the ownership can only be proven once to the public (e.g., to the court). After that, the secret key is known to the public and the embedded watermark can be easily destroyed by malicious users. Moreover, most of the existing techniques introduce distortions to the underlying data in the watermarking process, either by modifying least significant bits or exchanging categorical values. The distortions inevitably reduce the value of the data. In this paper, we propose a watermarking scheme by which the ownership of data can be publicly proven by anyone, as many times as necessary. The proposed scheme is distortion-free, thus suitable for watermarking any type of data without fear of error constraints. The proposed scheme is robust against typical database attacks including tuple/attribute insertion/deletion, random/selective value modification, data frame-up, and additive attacks.
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
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.
18701856
Protecting RFID communications in supply chains
Recent years have seen much growing attention on RFID security. However, little work has been performed to address the security issues in the context of supply chain management, which is exactly the major field for RFID applications. Existing RFID solutions cannot be applied directly in this field because of a set of special RFID security requirements to be addressed for supply chain management. The major contribution of this paper is to identify the unique set of security requirements in supply chains and to propose a practical design of RFID communication protocols that satisfy the security requirements.
3388099
Compressing and companding high dynamic range images with subband architectures
High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multi-scale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of "companding", in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.
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.
7384853
Consistent line clusters for building recognition in CBIR
This paper introduces a new mid-level feature, the consistent line cluster for use in content-based image retrieval. The color, orientation, and spatial features of line segments are exploited to group them into line clusters. The interrelationships among different clusters and the intra relationships within single clusters are used to recognize and roughly locate buildings in photographic images. Experiments are performed on a database of color images of outdoor scenes.
6040880
Rupture degree of graphs
We introduce a new graph parameter, the rupture degree. The rupture degree for a complete graph K n is defined as 1−n, and the rupture degree for an incomplete connected graph G is defined by r(G)=max{ω(G−X)−|X|−m(G−X):X⊂V(G), ω(G−X)>1}, where ω(G−X) is the number of components of G−X and m(G−X) is the order of a largest component of G−X. It is shown that this parameter can be used to measure the vulnerability of networks. Rupture degrees of several specific classes of graphs are determined. Formulas for the rupture degree of join graphs and some bounds of the rupture degree are given. We also obtain some Nordhaus–Gaddum type results for the rupture degree.
16081168
Sample dependence correction for order selection in fMRI analysis
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study the brain function. The selection of the proper number of signals of interest is an important step in the analysis to reduce the risk of over/underfilling. The inherent sample dependence in the spatial or temporal dimension of the fMRI data violates the assumption of independent and identically distributed (i.i.d.) samples and limits the usefulness of the practical formulations of information-theoretic order selection criteria. We propose a novel method using an entropy rate matching principle to mitigate the effects of such sample dependence in order selection. We perform order selection experiments on the simulated fMRI data and show that the incorporation of the proposed method significantly improves the accuracy of the order selection by different criteria. We also use the proposed method to estimate the number of latent sources in fMRI data acquired from multiple subjects performing a visuomotor paradigm. We show that the proposed method improves the order selection by alleviating the over-estimation due to the intrinsic smoothness and the effect of smooth preprocessing on the fMRI data