Emerge: Self-Emerging Data Release Using Cloud Data Storage

In the age of Big Data, advances in distributed technologies and cloud storage services provide highly efficient and cost-effective solutions to large scale data storage and management. Supporting self-emerging data using clouds is a challenging problem. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust. Supporting attack-resilient timed release of encrypted data stored in clouds requires new mechanisms for self emergence of data encryption keys that enables encrypted data to become accessible at a future point in time. Prior to the release time, the encryption key remains undiscovered and unavailable in a secure distributed system, making the private data unavailable. In this paper, we propose Emerge, a self-emerging timed data release protocol for securely hiding data encryption keys of private encrypted data in a large-scale Distributed Hash Table (DHT) network that makes the data available and accessible only at the defined release time. We develop a suite of erasure-coding-based routing path construction schemes for securely storing and routing encryption keys in DHT networks that protect an adversary from inferring the encryption key prior to the release time (release-ahead attack) or from destroying the key altogether (drop attack). Through extensive experimental evaluation, we demonstrate that the proposed schemes are resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network’s lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network’s lifespan improved which can preserve the sensor’s energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed

Differentially Private Trajectory Analysis for Points-of-Interest Recommendation

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet ϵ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees

ReverseCloak: A Reversible Multi-level Location Privacy Protection System

With the fast popularization of mobile devices and wireless networks, along with advances in sensing and positioning technology, we are witnessing a huge proliferation of Location-based Services (LBSs). Location anonymization refers to the process of perturbing the exact location of LBS users as a cloaking region such that a user's location becomes indistinguishable from the location of a set of other users. However, existing location anonymization techniques focus primarily on single level unidirectional anonymization, which fails to control the access to the cloaking data to let data requesters with different privileges get information with varying degrees of anonymity. In this demonstration, we present a toolkit for ReverseCloak, a location perturbation system to protect location privacy over road networks in a multi-level reversible manner, consisting of an 'Anonymizer' GUI to adjust the anonymization settings and visualize the multilevel cloaking regions over road network for location data owners and a 'De-anonymizer' GUI to de-anonymize the cloaking region and display the reduced region over road network for location data requesters. With the toolkit, we demonstrate the practicality and effectiveness of the ReverseCloak approach

Switching pulse generation for DC-DC boost converter using Xilinx-ISE with FPGA processor

This paper explains steps to generate switching pulse using Xilinx-ISE with FPGA processor for DC-DC boost converter. The switching pulse generated using Very high speed integrated circuit Hardware Description Language (VHDL) with Xilinx-ISE. VHDL is a programming language, which is used to model and design any complex circuits in a dynamic environment. This paper gives the course of action for generation of switching pulses for dc-dc boost converter using Xilinx-ISE and matlab simulink. The switching pulse generated using Xilinx-ISE with FPGA-Spartan 6 processor compared with switching pulse generated using matlab

Targeting IκappaB kinases for cancer therapy

The inhibitory kappa B kinases (IKKs) and IKK related kinases are crucial regulators of the pro-inflammatory transcription factor, nuclear factor kappa B (NF-κB). The dysregulation in the activities of these kinases has been reported in several cancer types. These kinases are known to regulate survival, proliferation, invasion, angiogenesis, and metastasis of cancer cells. Thus, IKK and IKK related kinases have emerged as an attractive target for the development of cancer therapeutics. Several IKK inhibitors have been developed, few of which have advanced to the clinic. These inhibitors target IKK either directly or indirectly by modulating the activities of other signaling molecules. Some inhibitors suppress IKK activity by disrupting the protein-protein interaction in the IKK complex. The inhibition of IKK has also been shown to enhance the efficacy of conventional chemotherapeutic agents. Because IKK and NF-κB are the key components of innate immunity, suppressing IKK is associated with the risk of immune suppression. Furthermore, IKK inhibitors may hit other signaling molecules and thus may produce off-target effects. Recent studies suggest that multiple cytoplasmic and nuclear proteins distinct from NF-κB and inhibitory κB are also substrates of IKK. In this review, we discuss the utility of IKK inhibitors for cancer therapy. The limitations associated with the intervention of IKK are also discussed

On the Accuracy of A.C. Flux Leakage, Eddy Current, EMAT and Ultrasonic Methods of Measuring Surface Connecting Flaws in Seamless Steel Tubing

The objective of this study was to perform a comparative experimental evaluation to determine the detection sensitivity, classification (fJaw type) and depth sizing accuracy of A.C. flux leakage, single-frequency eddy current, electromagnetic acoustic transducer (EMAT) generated surface waves, and broadband ultrasonic methods for the measurement of complex surface connecting flaws in hot rolled, seamless, ferritic tubing. Since it was of interest to invest NDE techniques over a wide range of capabilities, tubing having flaw depths far exceeding industry standards was tested and evaluated. Results of the study will be used to provide a benchmark assessment of these NDE methods, from which decisions concerning production test systems can be made

Timed-Release of Self-Emerging Data Using Distributed Hash Tables

Releasing private data to the future is a challenging problem. Making private data accessible at a future point in time requires mechanisms to keep data secure and undiscovered so that protected data is not available prior to the legitimate release time and the data appears automatically at the expected release time. In this paper, we develop new mechanisms to support self-emerging data storage that securely hide keys of encrypted data in a Distributed Hash Table (DHT) network that makes the encryption keys automatically appear at the predetermined release time so that the protected encrypted private data can be decrypted at the release time. We show that a straight-forward approach of privately storing keys in a DHT is prone to a number of attacks that could either make the hidden data appear before the prescribed release time (release-ahead attack) or destroy the hidden data altogether (drop attack). We develop a suite of self-emerging key routing mechanisms for securely storing and routing encryption keys in the DHT. We show that the proposed scheme is resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks. Our experimental evaluation demonstrates the performance of the proposed schemes in terms of attack resilience and churn resilience

SYNTHESIS AND IN-SILICO ANTI-INFLAMMATORY INVESTIGATION OF 2, 3-DIHYDROCHROMEN-4-ONE AND 3, 4-DIHYDROBENZO[B]OXEPIN-5(2H)-ONE BASED PYRAZOLE DERIVATIVES

This study synthesized six pyrazole derivatives from the key intermediates 2,3-dihydrochromen-4-one and 3,4-dihydrobenzo[b]oxepin-5(2H)-one. We have characterized all pyrazole derivatives as well as conducted in silico anti-inflammatory studies. The DFT calculations were performed using Gaussian 09 software. The compound 9 has the lowest energy gap (∆E, 1.0698 eV), lowest hardness (0.5349 eV), highest softness (1.8695 eV), and highest electrophilicity (7.0809eV) among all pyrazole derivatives and standard Aspirin. Swiss ADME software was used to carry out the ADME analysis. The chloro-substituted pyrazole derivatives (5, 6, and 9) were non-toxic, however, the nitrogen-substituted pyrazole derivatives (10, 13 and 14) and Aspirin were toxic. The docking patterns of the pyrazole derivatives with COX-2 selective inhibitors proteins (5F19) have been studied. Compound 9 has the lower binding energy (-10.2Kcal/mol) as compared with that of other pyrazole derivatives and standard Aspirin drugs. As a result, the pyrazole derivatives compound 9 is a promising anti-inflammatory drug with selective COX-2 inhibition as compared to the Aspirin drugs physicochemical properties