Shampa Chakraverty

Watermarking relational databases using bacterial foraging algorithm

The main aspect of database protection is to prove the ownership of data that describes who is the originator of data. It is of particular importance in the case of electronic data, as data sets are often modified and copied without proper citation or acknowledgement of originating data set. We present a novel method for watermarking relational databases for identification and proof of ownership based on the secure embedding of blind and multi-bit watermarks using Bacterial Foraging Algorithm (BFA). Feasibility of BFA implementation is shown in the framed watermarking databases application. Identification of owner is cryptographically made secure and used as an embedded watermark. An improved hash partitioning approach is used that is independent of primary key of the database to secure ordering of the tuples. Strength of BFA is explored to make the technique robust, secure and imperceptible. BFA is implemented to give nearly global optimal values bounded by data usability constraints and thus makes database fragile to any attack. The parameters of BFA are tuned to reduce the execution time. BFA is experimentally proved to be better solution than Genetic Algorithm (GA). The technique proposed is experimentally proved to be resilient against malicious attacks.

Adaptive Forward Error Correction (AFEC) based Streaming using RTSP and RTP

Real Time streaming Protocol (RTSP) is the de facto standard for streaming and almost all the set up boxes and media players support it. RTSP is a very simple text based protocol for streaming. And this simplicity is one of the main reasons for the popularity of the protocol. We propose the use of RTSP along with Real Time Protocol (RTP) for Adaptive Forward Error Correction (AFEC) based streaming. The idea is to use the existing semantics of the protocol with minimal overloading to achieve the goal. The use of the RTP is solely to provide a mechanism for the packet sequence number, which is the main requirement to ensure reliability. Since, we will be using existing protocol semantics (overloading the existing semantics), we do not have the luxury of using Negative Acknowledgements (NACKs) or information rich packet headers. However, we can add the intelligence in the client/server to interpret the overloaded data and achieve the same results.

Supervised Metaphor Detection using Conditional Random Fields

In this paper, we propose a novel approach for supervised classification of linguistic metaphors in an open domain text using Conditional Random Fields (CRF). We analyze CRF based classification model for metaphor detection using syntactic, conceptual, affective, and word embeddings based features which are extracted from MRC Psycholinguistic Database (MRCPD) and WordNet-Affect. We use word embeddings given by Huang et al. to capture information such as coherence and analogy between words. To tackle the bottleneck of limited coverage of psychological features in MRCPD, we employ synonymy relations from WordNet ® . A comparison of our approach with previous approaches shows the efficacy of CRF classifier in detecting metaphors. The experiments conducted on VU Amsterdam metaphor corpus provides an accuracy of more than 92% and Fmeasure of approximately 78%. Results shows that inclusion of conceptual features improves the recall by 5% whereas affective features do not have any major impact on metaphor detection in open text.

A Scheme for Robust Biometric Watermarking in Web Databases for Ownership Proof with Identification

We propose a robust technique for watermarking relational databases using voice as a biometric identifier of ownership. The choice of voice as the distinguishing factor is influenced by its uniqueness, stability, universality, and ease of use. The watermark is generated by creating a statistical model of the features extracted from the owner’s voice. This biometric watermark is then securely embedded into selected positions of the fractional parts of selected numeric attributes using a reversible bit encoding technique. In case of a dispute regarding true ownership, the relative scores of the extracted watermark are generated by comparing features of the disputed voices with the extracted one. We experimentally demonstrate the robustness of the proposed technique against various attacks. Results show that watermark is extracted with 100% accuracy even when 97% tuples are added or when 90% tuples are deleted or when 80% tuples are altered. More significantly, biometric identification helped identify the correct owner even when only 60% of the watermark could be extracted.

State model for scheduling Built-in Self-Test and scrubbing in FPGA to maximize the system availability in space applications

Reconfigurable Field Programmable Gate Arrays (rFPGAs) are employed extensively in spacecraft electronic systems to implement low-power adaptable systems that provide high density functionality. A challenge that must be tackled during system design is their high susceptibility to radiation induced Single Event Upsets (SEUs). A burst of energized particles may cause extensive damage to circuits. Even if their presence is transient, SEU faults may cause a permanent failure when they afflict the configuration memory — SRAM. There are two ways in which timely detection of faults and timely action to circumvent them can be undertaken — (i) Online error detecting/correcting circuits that demand a high premium in terms of FPGA area for extending error-security and Fault Tolerance, thus increasing cost and redundancy, and (ii) Built-in Self-Test (BIST) that allows efficient and very high-coverage fault detection but the fault testing is performed off-line. In general, Reliability and Availability (R&A) is a crucial quality parameter. In this paper, we present a state model that schedules Self-tests judiciously to optimize availability while ensuring a high degree of reliability. The aim of system design is to determine an optimal value of the mean time to self testing λT so that faults are detected on time and are reconfigured to boost system availability.

A rule-based availability-driven cosynthesis scheme

Abstract The co-synthesis of hardware–software systems for complex embedded applications has been studied extensively with focus on various qualitative system objectives such as high speed performance and low power dissipation. One of the main challenges in the construction of multiprocessor systems for complex real time applications is provide high levels of system availability that satisfies the users’ expectations. Even though the area of hardware software cosynthesis has been studied extensively in the recent past, the issues that specifically relate to design exploration for highly available architectures need to be addressed more systematically and in a manner that supports active user participation. In this paper, we propose a user-centric co-synthesis mechanism for generating gracefully degrading, heterogeneous multiprocessor architectures that fulfills the dual objectives of achieving real-time performance as well as ensuring high levels of system availability at acceptable cost. A flexible interface allows the user to specify rules that effectively capture the users’ perceived availability expectations under different working conditions. We propose an algorithm to map these user requirements to the importance attached to the subset of services provided during any functional state. The system availability is evaluated on the basis of these user-driven importance values and a CTMC model of the underlying fail-repair process. We employ a stochastic timing model in which all the relevant performance parameters such as task execution times, data arrival times and data communication times are taken to be random variables. A stochastic scheduling algorithm assigns start and completion time distributions to tasks. A hierarchical genetic algorithm optimizes the selections of resources, i.e. processors and busses, and the task allocations. We report the results of a number of experiments performed with representative task graphs. Analysis shows that the co-synthesis tool we have developed is effectively driven by the user’s availability requirements as well as by the topological characteristics of the task graph to yield high quality architectures. We experimentally demonstrate the edge provided by a stochastic timing model in terms of performance assessment, resource utilization, system-availability and cost.

Enabling information recovery with ownership using robust multiple watermarks

With the increasing use of databases, there is an abundant opportunity to investigate new watermarking techniques that cater to the requirements for emerging applications. A major challenge that needs to be tackled is to recover crucial information that may be lost accidentally or due to malicious attacks on a database that represents asset and needs protection. In this paper, we elucidate a scheme for robust watermarking with multiple watermarks that resolve the twin issues of ownership and recovery of information in case of data loss. To resolve ownership conflicts watermark is prepared securely and then embedded into the secretly selected positions of the database. Other watermark encapsulates granular information on user-specified crucial attributes in a manner such that the perturbed or lost data can be regenerated conveniently later. Theoretical analysis proves that the probability of identifying target locations, False hit rate and False miss rate is negligible. We have experimentally verified that the proposed technique is robust enough to extract the watermark accurately even after 100% tuple addition or alteration and after 98% tuple deletion. Experiments on information recovery reveal that successful regeneration of tampered/lost data improves with the increase in the number of candidate attributes for embedding the watermark.

A design pattern for symmetric encryption

Symmetric encryption is widely used to enforce data security and provide other cryptographic primitives. Despite significant research that has led to the development of sophisticated symmetric encryption algorithms, very little has been done to develop systematic frameworks that can support the creation of adaptable, application-specific encryption techniques with varying levels of security. In this paper, we tackle this challenge by proposing a new Circular Design Pattern (CDP) which serves as a template for generating Dynamic Symmetric Encryption Frameworks (DSEF). The layout of a DSEF remains the same but the underlying algorithms may vary during run time. We illustrate how the proposed CDP can be used to create new DSEF from the set of existing algorithms. We propose a modified version of conventional cryptosystem model to deploy the DSEF. We analyze the enhanced security level of any new symmetric encryption algorithm generated by the CDP. We also present a comparison of the proposed CDP with some existing design patterns that may be used for creating DSEF.

Offline Adaptive Forward Error Correction (AFEC) for Reliable Multicast

The use of FEC/AFEC (adaptive forward error correction) for reliable multicast is not new. However, they are still not widely accepted as the de facto standard for recovering lost data. The primary reason is the added CPU overhead involved in generating the parity data. In this paper, we propose a technique to move the generation of AFEC parity data offline, on the server side. Thus, avoiding any CPU overhead associated with parity data generation. The challenge on the server side would be to meet the expectations of all the clients for a particular multicast. Since different clients may experience different loss rates at the same time, the challenge is to satisfy their needs in an optimal way without causing network congestion

Metaphor Detection Using Fuzzy Rough Sets

Recent works in metaphor detection highlight the importance of psychological features such as imageability and concreteness to identify metaphors in text. However, the aspect of imprecision that is intrinsic to cognitive concepts is yet to be explored fully. Furthermore, psychological features give us an approximate indication of whether a particular textual usage is metaphorical or not. In this paper, we reflect upon the problem of the inherent vagueness in psychological features and approximation in classification through the notion of fuzzy rough sets. We develop a fuzzy-rough rule-based classifier to detect metaphors in text and evaluate the performance of the proposed model on a dataset of nominal metaphors. The results indicate the suitability of incorporating fuzzy-rough sets over SVM and the traditional rough set model.