G(its)(2) VSR: An Information Theoretic Secure Verifiable Secret Redistribution Protocol for Long-term Archival Storage

Protocols for secure archival storage are becoming increasingly important as the use of digital storage for sensitive documents is gaining wider practice. Wong et al.[8] combined verifiable secret sharing with proactive secret sharing without reconstruction and proposed a verifiable secret redistribution protocol for long term storage. However their protocol requires that each of the receivers is honest during redistribution. We proposed[3] an extension to their protocol wherein we relaxed the requirement that all the recipients should be honest to the condition that only a simple majority amongst the recipients need to be honest during the re(distribution) processes. Further, both of these protocols make use of Feldman's approach for achieving integrity during the (redistribution processes. In this paper, we present a revised version of our earlier protocol, and its adaptation to incorporate Pedersen's approach instead of Feldman's thereby achieving information theoretic secrecy while retaining integrity guarantees.

ADAMIS: a database for medical information systems

This paper describes the design and implementation of ADAMIS (‘A database for medical information systems’). ADAMIS is a relational database management system for a general hospital environment. Apart from the usual database (DB) facilities of data definition and data manipulation, ADAMIS supports a query language called the ‘simplified medical query language’ (SMQL) which is completely end-user oriented and highly non-procedural. Other features of ADAMIS include provision of facilities for statistics collection and report generation. ADAMIS also provides adequate security and integrity features and has been designed mainly for use on interactive terminals.

Interval Data Classification under Partial Information: A Chance-Constraint Approach

This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.

Discovering mentorship information from author collaboration networks

Researchers are assessed from a researcher-centric perspective - by quantifying a researcher's contribution to the field. Citation and publication counts are some typical examples. We propose a student-centric measure to assess researchers on their mentoring abilities. Our approach quantifies benefits bestowed by researchers upon their students by characterizing the publication dynamics of research advisor-student interactions in author collaboration networks. We show that our measures could help aspiring students identify research advisors with proven mentoring skills. Our measures also help in stratification of researchers with similar ranks based on typical indices like publication and citation counts while being independent of their direct influences.

Information and Decision Technologies

Production scheduling in a flexible manufacturing system (FMS) is a real-time combinatorial optimization problem that has been proved to be NP-complete. Solving this problem needs on-line monitoring of plan execution and requires real-time decision-making in selecting alternative routings, assigning required resources, and rescheduling when failures occur in the system. Expert systems provide a natural framework for solving this kind of NP-complete problems.In this paper an expert system with a novel parallel heuristic approach is implemented for automatic short-term dynamic scheduling of FMS. The principal features of the expert system presented in this paper include easy rescheduling, on-line plan execution, load balancing, an on-line garbage collection process, and the use of advanced knowledge representational schemes. Its effectiveness is demonstrated with two examples.

Missile battery placement for air defense: A dynamic programming approach

A procedure to evaluate surface-to-air missile battery placement patterns for air defense is presented. A measure of defense effectiveness is defined as a function of kill probability of the defense missiles and the nature of the surrounding terrain features. The concept of cumulative danger index is used to select the best path for a penetrating hostile aircraft for any given pattern of placement. The aircraft is assumed to be intelligent and well-informed. The path is generated using a dynamic programming methodology. The software package so developed can be used off-line to choose the best among a number of possible battery placement patterns.

On the Decidability of Model-Checking Information Flow Properties

Current standard security practices do not provide substantial assurance about information flow security: the end-to-end behavior of a computing system. Noninterference is the basic semantical condition used to account for information flow security. In the literature, there are many definitions of noninterference: Non-inference, Separability and so on. Mantel presented a framework of Basic Security Predicates (BSPs) for characterizing the definitions of noninterference in the literature. Model-checking these BSPs for finite state systems was shown to be decidable in [8]. In this paper, we show that verifying these BSPs for the more expressive system model of pushdown systems is undecidable. We also give an example of a simple security property which is undecidable even for finite-state systems: the property is a weak form of non-inference called WNI, which is not expressible in Mantel’s BSP framework.

A sharable instructable agent for information filtering

In this paper we present an information filtering agent called sharable instructable information filtering agent (SIIFA). It adopted the approach of sharable instructable agents. SIIFA provides comprehensible and flexible interaction to represent and filter the documents. The representation scheme in SIIFA is personalized. It, either fully or partly, can be shared among the users of the stream while not revealing their interests and can be easily edited. SIIFA is evaluated on the comp.ai.neural-nets Usent newsgroup documents and compared with the vector space method.

Information-theoretically secure regenerating codes for distributed storage

Regenerating codes are a class of codes for distributed storage networks that provide reliability and availability of data, and also perform efficient node repair. Another important aspect of a distributed storage network is its security. In this paper, we consider a threat model where an eavesdropper may gain access to the data stored in a subset of the storage nodes, and possibly also, to the data downloaded during repair of some nodes. We provide explicit constructions of regenerating codes that achieve information-theoretic secrecy capacity in this setting.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Model-checking bisimulation-based information flow properties for infinite state sstems

Bisimulation-based information flow properties were introduced by Focardi and Gorrieri [1] as a way of specifying security properties for transition system models. These properties were shown to be decidable for finite-state systems. In this paper, we study the problem of verifying these properties for some well-known classes of infinite state systems. We show that all the properties are undecidable for each of these classes of systems.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

A Multi-phase Approach for Improving Information Diffusion in Social Networks

For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.