Screening of polymeric membranes for membrane assisted deacidification of sardine oil

The diversification in fish oil use and the need for softer processing demand new oil refining processes. In considering the advantages of membrane technology, three different membranes (polyamide (PA), polytetrafluoroethylene (PTFE) and polyethersulfone (PES)) were used in this particular study. Preliminary results in the separation of free fatty acids (FFA) from glycerides of sardine oil/ethanol mixtures using a single dead end microfiltration mode have been reported here. The influence of experimental factors like pressure and oil/ethanol ratios (w/v) on the permeate flux and the reduction of FFA (%) in the permeate was studied. PTFE membrane showed promising results in terms of residual FFA in permeate (%), % oil loss (15.12%, 5.45%) as compared to PA (20.50%, 6.66%) and PES membranes (20.60%, 8.92%). PA membrane showed a higher flux of 4.4 L/m2 /h, followed by PTFE 3.34 L/m2 /h and PES, 1.19 L/m2 /h at 3.5 bar trans-membrane pressure. These results showed that using PTFE membrane could be an ideal strategy for the membrane assisted deacidification of sardine oil using solvents

Developing Use Cases and State Transition Models for Effective Protection of Electronic Health Records (EHRs) in Cloud

ABSTRACT: This paper proposes new object oriented design of use cases and state transition models to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information disclosures they are: Identity disclosure and Attribute disclosure. Identity disclosure occurs when an individual's linked to a particular record in the released Attribute disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, use cases and state transition models of the system proposed

Investigations on Methods Developed for Effective Discovery of Functional Dependencies

ABSTRACT: This paper details about various methods to discover functional dependencies from data.Effective pruning for the discovery of conditional functional dependencies is discussed in detail. Di conditional Functional Dependencies and Fast FDs a heuristic-driven, Depth-first algorithm for mining FD from relation instances are elaborated. Privacy preserving publishing micro data with Full Functional Dependencies and Conditional functional dependencies for capturing data inconsistencies are examined. The approximation measures for functional dependencies and the complexity of inferring functional dependencies are also observed. Compression -Based Evaluation of partial determinations is portrayed. This survey would promote a lot of research in the area of mining functional dependencies from data

A Novel Activity Flow Model for Effective Protection of Electronic Health Records (EHRs) in Cloud

ABSTRACT: This paper proposes new methods to effectively guard Electronic Health Records (EHRs). Privacy-An important factor need to be considered while we publishing the microdata. Usually government agencies and other organization used to publish the microdata. On releasing the microdata, the sensitive information of the individuals are being disclosed. This constitutes a major problem in the government and organizational sector for releasing the microdata. In order to sector or to prevent the sensitive information, we are going to implement certain algorithms and methods. Normally there two types of information disclosures they are: Identity disclosure and Attribute disclosure. Identity disclosure occurs when an individual's linked to a particular record in the released Attribute disclosure occurs when new information about some individuals are revealed. This paper aims to discuss the existing techniques present in literature for preserving, incremental development, activity flow model and modular workflow model of the system proposed

Survey on Security on Cloud Computing by Trusted Computer Strategy

ABSTRACT: This paper reviews methods developed for anonymizing data from 2009 to 2010. Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent attribute disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive attribute across equivalence class can substantially improve the privacy as against information disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive attribute in a microdata and also maintaining diversity

Detailed Investigation on Strategies Developed for Effective Discovery of Matching Dependencies

ABSTRACT: This paper details about various methods prevailing in literature for efficient discovery of matching dependencies. The concept of matching dependencies (MDs) has recently been proposed for specifying matching rules for object identification. Similar to the functional dependencies with conditions, MDs can also be applied to various data quality applications such as detecting the violations of integrity constraints. The problem of discovering similarity constraints for matching dependencies from a given database instance is taken into consideration. This survey would promote a lot of research in the area of information mining

Further Investigations on Strategies Developed for Efficient Discovery of Matching Dependencies

ABSTRACT: This paper details about various methods prevailing in literature for efficient discovery of matching dependencies. The concept of matching dependencies (MDs) has recently been proposed for specifying matching rules for object identification. Similar to the functional dependencies with conditions, MDs can also be applied to various data quality applications such as detecting the violations of integrity constraints. The problem of discovering similarity constraints for matching dependencies from a given database instance is taken into consideration. This survey would promote a lot of research in the area of information mining

Investigations on Evolution of Approaches Developed for Data Privacy

ABSTRACT: This paper reviews methods developed for anonymizing data from 1984 to 1988 . Publishing microdata such as census or patient data for extensive research and other purposes is an important problem area being focused by government agencies and other social associations. The traditional approach identified through literature survey reveals that the approach of eliminating uniquely identifying fields such as social security number from microdata, still results in disclosure of sensitive data, k-anonymization optimization algorithm ,seems to be promising and powerful in certain cases ,still carrying the restrictions that optimized k-anonymity are NP-hard, thereby leading to severe computational challenges. k-anonimity faces the problem of homogeneity attack and background knowledge attack . The notion of ldiversity proposed in the literature to address this issue also poses a number of constraints , as it proved to be inefficient to prevent attribute disclosure (skewness attack and similarity attack), l-diversity is difficult to achieve and may not provide sufficient privacy protection against sensitive attribute across equivalence class can substantially improve the privacy as against information disclosure limitation techniques such as sampling cell suppression rounding and data swapping and pertubertation. This paper aims to discuss efficient anonymization approach that requires partitioning of microdata equivalence classes and by minimizing closeness by kernel smoothing and determining ether move distances by controlling the distribution pattern of sensitive attribute in a microdata and also maintaining diversity