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 of Smart Door Closer System with Image Classification over WLAN

Wireless Personal Communications - A dual purpose system is presented in this paper which serves not only as a door closer, but is equally effective for surveillance purposes. The currently...     

Springback analysis of thin rectangular bars of non-linear work-hardening materials under torsional loading

A theoretical analysis of the springback of narrow rectangular strips of non-linear work-hardening material under torsional loading has been carried out. This theoretical analysis is supported by experimental results for rectangular mild steel strips of different thicknesses and lengths. Finally an analytical generalized expression relating angle of twist to twisting moment and residual angle of twist per unit length for rectangular strips under plastic torsion is obtained in non-dimensionalized form. A comparison between the results obtained for bars of non- linear and linear work-hardening materials loaded under torsion is also made.     

A Convenient Synthesis of Some Coumarin Derivatives Using SnCl2 · 2H2O as Catalyst

Some substituted coumarins have been synthesized by von-Pechmann condensation using SnCl₂ · 2H₂O (10 mol %) as catalyst in ethanolic medium. The reactions are simple, easy in handling and environmentally benign.     

Instron texture profile of buffalo milk cheddar cheese as influenced by composition and ripening changes

Buffalo milk Cheddar cheese samples of different ages were analysed for compositional attributes (CA), ripening indices (RI) and Instron Textural Profile (ITP). All samples were compositionally alike, except for pH and salt-in-moisture (SM) contents. RI showed significant variations. CA and RI showed highly significant correlations within themselves and with each other, except for moisture with pH, SM with moisture, MNFS, Fat and FDM and Fat with MNFS. The ITPs of cheeses showed significant variations and had highly significant intercorrelations indicating their interdependence. CA (except moisture and MNFS) and RI showed a highly significant correlationship with ITPs. Moisture content showed a highly significant correlationship with all ITPs, except cohesiveness and springiness, where it was significant. MNFS content showed significant correlations only with hardness and brittleness. Stepwise regression analysis revealed that MI was the most predominant factor influencing cheese texture, followed by pH, SM, FDM and TVFA. Knowing Ca and RI, the textural properties of cheeses can be forecast through mathematical equations. Similarly the age of cheese can also be predicted if RI and/or textural properties are known.     

Estimation of elastic constant of rocks using an ANFIS approach

The engineering properties of the rocks have the most vital role in planning of rock excavation and construction for optimum utilization of earth resources with greater safety and least damage to surroundings. The design and construction of structure is influenced by physico-mechanical properties of rock mass. Young's modulus provides insight about the magnitude and characteristic of the rock mass deformation due to change in stress field. The determination of the Young's modulus in laboratory is very time consuming and costly. Therefore, basic rock properties like point load, density and water absorption have been used to predict the Young's modulus. Point load, density and water absorption can be easily determined in field as well as laboratory and are pertinent properties to characterize a rock mass. The artificial neural network (ANN), fuzzy inference system (FIS) and neuro fuzzy are promising techniques which have proven to be very reliable in recent years. In, present study, neuro fuzzy system is applied to predict the rock Young's modulus to overcome the limitation of ANN and fuzzy logic. Total 85 dataset were used for training the network and 10 dataset for testing and validation of network rules. The network performance indices correlation coefficient, mean absolute percentage error (MAPE), root mean square error (RMSE), and variance account for (VAF) are found to be 0.6643, 7.583, 6.799, and 91.95 respectively, which endow with high performance of predictive neuro-fuzzy system to make use for prediction of complex rock parameter.