A privacy-preserving model based on differential approach for sensitive data in cloud environment

A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.

     

Design methodologies for high-performance noise-tolerant XOR-XNOR circuits

Scaling down to deep submicrometer (DSM) technology has made noise a metric of equal importance as compared to power, speed, and area. Smaller feature size, lower supply voltage, and higher frequency are some of the characteristics for DSM circuits that make them more vulnerable to noise. New designs and circuit techniques are required in order to achieve robustness in presence of noise. Novel methodologies for designing energy-efficient noise-tolerant exclusive-OR-exclusive- NOR circuits that can operate at low-supply voltages with good signal integrity and driving capability are proposed. The circuits designed, after applying the proposed methodologies, are characterized and compared with previously published circuits for reliability, speed and energy efficiency. To test the driving capability of the proposed circuits, they are embedded in an existing 5-2 compressor design. The average noise threshold energy (ANTE) is used for quantifying the noise immunity of the proposed circuits. Simulation results show that, compared with the best available circuit in literature, the proposed circuits exhibit better noise-immunity, lower power-delay product (PDP) and good driving capability. All of the proposed circuits prove to be faster and successfully work at all ranges of supply voltage starting from 3.3 V down to 0.6 V. The savings in the PDP range from 94% to 21% for the given supply voltage range respectively and the average improvement in the ANTE is 2.67X.     

Situating natural language understanding within experience-based design

Building useful systems with an ability to understand "real" natural language input has long been an elusive goal for Artificial Intelligence. Well-known problems such as ambiguity, indirectness, and incompleteness of natural language inputs have thwarted efforts to build natural language interfaces to intelligent systems. In this article, we report on our work on a model of understanding natural language design specifications of physical devices such as simple electrical circuits. Our system, called KA, solves the classical problems of ambiguity, incompleteness and indirectness by exploiting the knowledge and problem-solving processes in the situation of designing simple physical devices. In addition, KA acquires its knowledge structures (apart from a basic ontology of devices) from the results of its problem-solving processes. Thus, KA can be bootstrapped to understand design specifications and user feedback about new devices using the knowledge structures it acquired from similar devices designed previously.In this paper, we report on three investigations in the KA project. Our first investigation demonstrates that KA can resolve ambiguities in design specifications as well as infer unarticulated requirements using the ontology, the knowledge structures, and the problem-solving processes provided by its design situation. The second investigation shows that KA's problem-solving capabilities help ascertain the relevance of indirect design specifications, and identify unspecified relations between detailed requirements. The third investigation demonstrates the extensibility of KA's theory of natural language understanding by showing that KA can interpret user feedback as well as design requirements. Our results demonstrate that situating language understanding in problem solving, such as device design in KA, provides effective solutions to unresolved problems in natural language processing.     

Effect of Nb doping on structural and electric properties of PZT (65/35) ceramic

The aim of this study is to determine the effect of Nb⁵⁺ doping on PZT (65/35) based bulk materials in their structure, micro structure and electrical properties. The Nb content was chosen 0-9 mole%. These materials were prepared by conventional mixed oxide method. X ray diffraction studies suggest the compound to be of rhombohedral perovskite phase. Excess addition of Nb result in pyrochlore and fluorite phase develops in PZT (65/35) sample. Detailed studies of dielectric constant as a function of temperature (40degC to 500degC) and frequency (100 Hz to 1 MHz) suggest that the compounds undergo diffuse type of phase transition. Maximum dielectric constant and resistivity were found to be strongly dependent on doping and measuring frequencies. Using complex impedance analysis micro structural parameters such as bulk and grain boundary resistance, bulk charge carrier concentration and relaxation time were determined     

Preparation of an anion‐exchange adsorbent by the radiation‐induced grafting of vinylbenzyltrimethylammonium chloride onto cotton cellulose and its application for protein adsorption

Poly(vinylbenzyltrimethylammonium chloride)‐graft‐cotton cellulose, an anion‐exchange matrix, was synthesized by a mutual radiation‐induced grafting technique with a ⁶⁰Co γ‐radiation source. The grafted matrix was characterized by grafting yield estimation, elemental analysis, Fourier transform infrared spectroscopy, and scanning electron microscopy. The grafting yield decreased with the increase in the dose rate. However, the grafting yield and nitrogen content of grafted samples increased almost linearly with an increase in the total irradiation dose. To evaluate the performance of the grafted anion‐exchange matrix, the protein adsorption and elution behavior were investigated in a continuous column process under various experimental conditions, with bovine serum albumin used as a model protein. The binding and elution behavior of the anion‐exchange matrix depended on different experimental parameters, such as the grafting yield, ionic strength, pH of the medium, and amount of protein loaded. From a breakthrough curve, the equilibrium binding capacity and elution percentage of the grafted anion‐exchange matrix were estimated to be 40 mg/g and 94%, respectively. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 5512–5521, 2006