Yogesh R. Kulkarni

Bio: Yogesh R. Kulkarni is an academic researcher. The author has contributed to research in topics: Data publishing & Computer science. The author has an hindex of 2, co-authored 3 publications receiving 7 citations.

Genetic grey wolf optimization and C-mixture for collaborative data publishing

Abstract: Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating...

Optimized key generation-based privacy preserving data mining model for secure data publishing

Abstract: • Proposed CSHGSO for secure data publishing: A robust technique is devised for the secret key generation for the secure data publishing using the proposed CSHGSO. The objective function for these optimization criteria is designed with respect to the privacy and utility measures, such as conditional privacy and accuracy. However, the proposed CSHGSO is devised by the integration of CSO and HGSO. • Furthermore, the performance of the newly devised CSHGSO is evaluated using two metrics, such as conditional privacy, and accuracy with the maximal conditional privacy of 1.338, and higher accuracy of 0.965, respectively. The data from the mobile devices and internet services plays a significant role now a day. The information shared needs to be original to get the best solution. Without the utilization of privacy preservation techniques, the individual's sensitive information cannot be exposed. The Privacy preserving data publishing (PPDP) is one of the privacy preserving techniques to preserve privacy. However, the reduction of the loss of data and the security enhancement are the major challenges. To solve the issues in secure data publishing, the efficient Cat Swarm Henry Gas Solubility Optimization (CSHGSO) is introduced for achieving effective privacy preservation for secure data publishing. In addition, the proposed CSHGSO is devised by the incorporation of Cat Swarm Optimization (CSO), and Henry Gas Solubility Optimization (HGSO). Here, the procedure of generating secret key is carried out based on the proposed CSHGSO with respect to the objective measures. The experimentation of the developed CSHGSO is performed in JAVA tool using Heart Disease Dataset. Furthermore, the CSHGSO achieved the higher conditional privacy of 1.338 using database-1, maximum accuracy of 0.965 using database-4, and the NMI value of 0.927 using database-4.

An Approach for Collaborative Data Publishing Using Self-adaptive Genetic Grey Wolf Optimizer

Abstract: This paper introduces an algorithm, termed self-adaptive genetic grey wolf optimizer (self-adaptive genetic GWO), for privacy preservation using a C-mixture factor. The C-mixture factor improves the privacy of data, in which the data does not satisfy the privacy constraints, such as l-diversity, m-privacy, and k-anonymity. Experimentation is carried out using the adult dataset, and the effectiveness of the proposed self-adaptive genetic GWO is checked depending on the information loss and the average equivalence class metric values and is evaluated to be the best when compared to other existing techniques with low information loss value as 0.1724 and average equivalence class value as 0.71, respectively.

Optimal cluster-based topology and Deep LSTM-based prediction method for data reduction in IoT

Abstract: In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001[Formula: see text]s, 0.161[Formula: see text]J and 99.054%, respectively.

Privacy Based Data Publishing Model for Cloud Computing Environment

Abstract: Cloud computing is a popular model for providing data storage services from remote computing facilities through internet. Security is known as an element for protecting sensitive information from vulnerable attacks and ensuring information confidentiality, integrity and authenticity. Privacy is the assurance that users could maintain complete control over their sensitive information. Cloud storage-based data publication is significant in medical field where it contains sensitive information such as nature of the disease, patient medical history, and effects of the illness. The publisher should not disclose any of the individual or sensitive information of the individuals with the research board while publishing the reports to the medical data analysts. Deciding on the nature of sensitivity, the user may be allowed to access the information from cloud environment that is a complex process. In order to ensure the complete privacy of individual medical history, the present research work employs k-anonymization to upgrade the privacy policies in the cloud storage. In addition to this, the genetic grey wolf optimization algorithm is employed to decide the data to be published based on the information preserved for privacy purposes. The proposed work is evaluated in a real cloud infrastructure with respect to privacy, utility and information losses. The results show that the proposed method is efficient for privacy-based data publication as it conceals the sensitive information effectively.

Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud

Abstract: Cloud systems are powerful computing resources used inevitably for data subscription and publication Even though the cloud platform can handle the huge volume of data, privacy becomes a critical issue during data publishing Hence, an effective technique for the privacy preservation of the data is required in the cloud computing environment Accordingly, this paper proposes a technique for privacy protection using the dyadic product and an optimization algorithm The privacy of the original database is protected by the construction of privacy preserved database using a dyadic square matrix obtained taking the dyadic product of two vectors, namely sensitive-utility (SU) coefficient and cumulative data key product The selection of SU coefficient vector is based on the proposed (Crow search based Lion) C-Lion algorithm, which is designed by combining crow search algorithm with lion algorithm The fitness of the proposed C-Lion algorithm is designed based on privacy and utility for the feasible selection of SU coefficient vector The performance of the proposed privacy protection technique based on the C-Lion algorithm is evaluated using two factors, privacy, and utility The experimental analysis shows that the proposed technique could attain the maximum utility of 0909 with privacy 0864 for the breast cancer dataset

Privacy Preservation of Electronic Health Record: Current Status and Future Direction

Abstract: Recent developments in health sector have made it possible to collect, store, manage, and share medical data in large scale. Managing and sharing of health record is primarily requirement in electronic health record software, however, reusability of electronic health records in distributive environment or access by third party must maintain principle of database system and implement the guidelines of international privacy policy standards and regulations. Privacy preservation is the major concern while dealing with real-time datasets in health sector. Privacy preservation algorithms have to ensure protection of sensitive information related to patients’ diagnoses and diseases. Privacy preserving data mining (PPDM) deals with data perturbation, anonymities, and modification as per the requirement of the system. Data perturbation is one of best PPDM techniques that basically deals with numeric values and focuses on privacy implementation. In this chapter, we will select and review different articles that are related to electronic health records (EHRs), their privacy standards, challenges, and regulations currently adopted in different countries. This chapter mainly reviews the current status of privacy preservation polices used in EHR, privacy techniques and analysis, and future scope of privacy in global scenario.

An Approach for Collaborative Data Publishing Using Self-adaptive Genetic Grey Wolf Optimizer

Abstract: This paper introduces an algorithm, termed self-adaptive genetic grey wolf optimizer (self-adaptive genetic GWO), for privacy preservation using a C-mixture factor. The C-mixture factor improves the privacy of data, in which the data does not satisfy the privacy constraints, such as l-diversity, m-privacy, and k-anonymity. Experimentation is carried out using the adult dataset, and the effectiveness of the proposed self-adaptive genetic GWO is checked depending on the information loss and the average equivalence class metric values and is evaluated to be the best when compared to other existing techniques with low information loss value as 0.1724 and average equivalence class value as 0.71, respectively.

COAT: COnstraint-based Anonymization of Transactions

Abstract: Publishing transactional data about individuals in an anonymous form is increasingly required by organizations. Recent approaches ensure that potentially identifying information cannot be used to link published transactions to individuals’ identities. However, these approaches are inadequate to anonymize data that is both protected and practically useful in applications because they incorporate coarse privacy requirements, do not integrate utility requirements, and tend to explore a small portion of the solution space. In this paper, we propose the first approach for anonymizing transactional data under application-specific privacy and utility requirements. We model such requirements as constraints, investigate how these constraints can be specified, and propose COnstraint-based Anonymization of Transactions, an algorithm that anonymizes transactions using a flexible anonymization scheme to meet the specified constraints. Experiments with benchmark datasets verify that COAT significantly outperforms the current state-of-the-art algorithm in terms of data utility, while being comparable in terms of efficiency. Our approach is also shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’ information.