Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of video-based applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting low-level visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are currently developing video component wrappers with well-defined interfaces to facilitate the modification or replacement of video processing components. The ultimate goal of the VDBMS project is a flexible, extensible framework that can be used by the research community for developing, testing, and benchmarking video database technologies.

In many image processing applications, edge detection is a useful method for obtaining a simplified image that preserves the domain geometric structures and spatial relationships among variant image components. For providing automatic edge detection, two problems should be solved: one is feature extraction for calculating the edge strength, another is feature classification for automatic edge detection. For solving these two problems, we propose an improved automatic edge detection technique. Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem. A fast entropic thresholding technique is also developed for solving the feature classification problem. Experimental results have confirmed that this proposed edge detector can provide more reasonable results as compared with the traditional isotropic edge operators, and its calculation cost has been reduced as compared with the complex edge detectors. Good balance between the calculation cost and the edge detection accuracy is achieved.

This paper gives an overview of the research and implementation challenges we encountered in building an end- to-end natural language processing based watermarking system. With natural language watermarking, we mean embedding the watermark into a text document, using the natural language components as the carrier, in such a way that the modifications are imperceptible to the readers and the embedded information is robust against possible attacks. Of particular interest is using the structure of the sentences in natural language text in order to insert the watermark. We evaluated the quality of the watermarked text using an objective evaluation metric, the BLEU score. BLEU scoring is commonly used in the statistical machine translation community. Our current system prototype achieves 0.45 BLEU score on a scale [0,1].

The demand for the secondary use of medical data is increasing steadily to allow for the provision of better quality health care. Two important issues pertaining to this sharing of data have to be addressed: one is the privacy protection for individuals referred to in the data; the other is copyright protection over the data. In this paper, we present a unified framework that seamlessly combines techniques of binning and digital watermarking to attain the dual goals of privacy and copyright protection. Our binning method is built upon an earlier approach of generalization and suppression by allowing a broader concept of generalization. To ensure data usefulness, we propose constraining binning by usage metrics that define maximal allowable information loss, and the metrics can be enforced off-line. Our watermarking algorithm watermarks the binned data in a hierarchical manner by leveraging on the very nature of the data. The method is resilient to the generalization attack that is specific to the binned data, as well as other attacks intended to destroy the inserted mark. We prove that watermarking could not adversely interfere with binning, and implemented the framework. Experiments were conducted, and the results show the robustness of the proposed framework.

A Generalized Temporal Role Based Access Control (GTRBAC) model that allows specification of a comprehensive set of temporal constraint for access control has recently been proposed. The model constructs allow one to specify various temporal constraints on role, user-role assignments and role-permission assignments. However, Temporal constraints on role enablings and role activations can have various implications on a role hierarchy. In this paper, we present an analysis of the effects of GTRBAC temporal constraints on a role hierarchy and introduce various kinds of temporal hierarchies. In particular, we show that there are certain distinctions that need to be made in permission inheritance and role activation semantics in order to capture all the effects of GTRBAC constraints such as role enablings and role activations on a role hierarchy.

Trust negotiation is a promising approach for establishing trust in open systems like the Internet, where sensitive interactions may often occur among entities with no prior knowledge of each other. In this article, the authors present a model for trust negotiation systems, and delineate the desiderata that ideal trust negotiation systems should satisfy. In defining trust negotiation requirements, they consider two different issues, policy language requirements and system requirements. They then survey the most interesting proposals that have been presented so far and evaluate them with respect to the identified requirements. Finally, they outline future research directions and identify the open issues that still have to be explored.

k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n2). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data.

A generalized temporal role based access control (GTRBAC) model that captures an exhaustive set of temporal constraint needs for access control has been proposed. GTRBAC’s language constructs allow one to specify various temporal constraints on role, user-role assignments and role-permission assignments. We present the notion of different types of role hierarchies based on the permission-inheritance and role activation semantics. In particular, we look at how new hierarchical relations between a pair of roles that are not directly related can be derived through other well-defined hierarchically related roles. When the different hierarchy types coexist in a role hierarchy, inferring such derived hierarchical relations between a pair of roles can be complex. The results presented provide a basis for formally analyzing the derived inheritance and activation semantics between every pair of roles in a hierarchy.

Data anonymization techniques based on the k-anonymity model have been the focus of intense research in the last few years. Although the k-anonymity model and the related techniques provide valuable solutions to data privacy, current solutions are limited only to static data release (i.e., the entire dataset is assumed to be available at the time of release). While this may be acceptable in some applications, today we see databases continuously growing everyday and even every hour. In such dynamic environments, the current techniques may suffer from poor data quality and/or vulnerability to inference. In this paper, we analyze various inference channels that may exist in multiple anonymized datasets and discuss how to avoid such inferences. We then present an approach to securely anonymizing a continuously growing dataset in an efficient manner while assuring high data quality.

Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user’s location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.

Electronic workplace surveillance is raising concerns about privacy and fairness. Integrating research on electronic performance monitoring, procedural justice, and organizational privacy, the author proposes a framework for understanding reactions to technologies used to monitor and control employees. To test the framework’s plausibility. temporary workers performed computer/Web-based tasks under varying levels of computer surveillance. Results indicated that monitoring job-relevant activities (relevance) and affording those who were monitored input into the process (participation) reduced invasion of privacy and enhanced procedural justice. Moreover, invasion of privacy fully mediated the effect of relevance and partially mediated the effect of participation on procedural justice. The findings are encouraging for integrating theory and research on procedural justice and organizational privacy.

We use uncertainty management theory (Lind & Van den Bos, 2002) as a framework to examine how the members of computer-mediated groups differ from those of face-to-face groups in their reactions to unfair events. Due to informational uncertainty surrounding interpersonal interactions in computer-mediated groups, fairness from authorities is more salient to the members of computer-mediated groups. Consequently, the members of computer-mediated groups tend to, in general, react more negatively to unfair events than do those of face-to-face groups. Moreover, the difference between the members of computer-mediated groups and face-to-face groups, in reactions to unfair events, increases over time. We present a laboratory study where we found support for these arguments.