Other affiliations: Indian Institute of Management Ahmedabad, Indian Institute of Management Lucknow
Bio: Saurabh Kumar is an academic researcher from Indian Institute of Management Indore. The author has contributed to research in topic(s): Futures contract & Ensemble forecasting. The author has an hindex of 5, co-authored 18 publication(s) receiving 64 citation(s). Previous affiliations of Saurabh Kumar include Indian Institute of Management Ahmedabad & Indian Institute of Management Lucknow.
Topics: Futures contract, Ensemble forecasting, Market microstructure, Information privacy, Volatility smile
TL;DR: It is revealed that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.
Abstract: Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trus...
TL;DR: The results from the study suggest that “legal consequences” and “technical measures” adopted for securing cyber-security in organisations are the most important antecedents for enhanced cyber- security levels in the organisations.
Abstract: PurposeThe present study aims to identify and investigate the antecedents of enhanced level of cyber-security at the organisational level from both the technical and the human resource perspective using human–organisation–technology (HOT) theory.Design/methodology/approachThe study has been conducted on 151 professionals who have expertise in dealing with cyber-security in organisations in sectors such as retail, education, healthcare, etc. in India. The analysis of the data is carried out using partial least squares based structural equation modelling technique (PLS-SEM).FindingsThe results from the study suggest that “legal consequences” and “technical measures” adopted for securing cyber-security in organisations are the most important antecedents for enhanced cyber-security levels in the organisations. The other significant antecedents for enhanced cyber-security in organisations include “role of senior management” and “proactive information security”.Research limitations/implicationsThis empirical study has significant implications for organisations as they can take pre-emptive measures by focussing on important antecedents and work towards enhancing the level of cyber-security.Originality/valueThe originality of this research is combining both technical and human resource perspective in identifying the determinants of enhanced level of cyber-security in the organisations.
TL;DR: The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing that is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure.
Abstract: With the advent of the online social network and advancement of technology, people get connected and interact on social network. To better understand the behavior of users on social network, we need to mine the interactions of users and their demographic data. Companies with less or no expertise in mining would need to share this data with the companies of expertise for mining purposes. The major challenge in sharing the social network data is maintaining the individual privacy on social network while retaining the implicit knowledge embedded in the social network. Thus, there is a need of anonymizing the social network data before sharing it to the third-party. The current study proposes to use upper approximation concept of rough sets for developing a solution for privacy preserving social network graph publishing. The proposed algorithm is capable of preserving the privacy of graph structure while simultaneously maintaining the utility or value that can be generated from the graph structure. The proposed algorithm is validated by showing its effectiveness on several graph mining tasks like clustering, classification, and PageRank computation. The set of experiments were conducted on four standard datasets, and the results of the study suggest that the proposed algorithm would maintain the both the privacy of individuals and the accuracy of the graph mining tasks.
Abstract: This project attempts to investigate the effect of the introduction of Futures trading in the National Stock Exchange, India (NSE) and get insights into the effect upon the volatility of the NSE. The underlying spot market volatility is estimated using symmetric GARCH methods. Any increase in stock market volatility that has followed the onset of futures trading has generally been taken as justifying the traditional view that the introduction of futures markets induces destabilizing speculation. This has led to calls for greater regulation to minimise any detrimental effects. An alternative view is that futures markets provide an additional route by which information can be transmitted, and, therefore, increased spot market volatility may simply be a consequence of the more frequent arrival, and more rapid processing of information. Thus, futures trading may be fully consistent with efficiently functioning markets. This paper attempts to investigate the change, if any, in the volatility observed in the Indian stock market due to the introduction of futures trading. The change in the volatility is compared not only in absolute levels of volatility but also in terms of the structure of the volatility. This is done to give insights into the way the futures market is influencing the Indian spot market's volatility.
Abstract: Industry 4.0 (I4) technologies are gaining increased importance in the manufacturing industry, as they can provide several benefits such as an increase in efficiency, lower costs, higher revenues, etc. This article empirically examines the critical success factors (CSF) for implementing I4 technologies in Indian automotive manufacturing industry. In this regard, CSF and performance outcomes of I4 technologies are identified from published literature. The relationships between CSF and performance outcomes are then examined by regression analysis. The results indicate that “Data governance” is the most critical factor, as it affects all the four performance outcomes (operational, product, economic, and responsiveness). Similarly, “Legal aspects” affects three out of the four performance outcomes (operational, product, and economic performance), while “Collaboration and teamwork” affects only operational performance and responsiveness. The study provides an understanding of factors which are critical in achieving performance outcomes about I4 technologies in the automotive manufacturing industry. The findings can also help automotive manufacturing firms toward an informed decision making in terms of various strategies required to adopt I4 technologies successfully.
TL;DR: Findings revealed that FC, TR and TA have a positive influence on intention to use BCSCM and regulatory support moderates the effect of FC.
Abstract: The behavioural intention to adopt Blockchain for supply chain management (BCSCM) is studied in this paper. The research framework adopted considers how Performance Expectancy (PE), Effort Expectan...
Abstract: The United States and its international partners are permitting an unregulated, global market for cyber weapons to flourish. Weaponized zero-day ("Oday") exploits to attack the control systems for the power grid and other critical infrastructure components are on sale to criminals, terrorists, and rogue nations. Policymakers have begun to recognize the imperative to curb this market. There is no consensus, however, on the measures needed to do so. We propose three initial steps to begin curbing the market for weaponized Oday exploits. First, the United States should incentivize developers of critical infrastructure industrial control systems and applications layer software to minimize security flaws in their products. The Support Anti-Terrorism by Fostering Effective Technologies Act provides an especially promising means to strengthen these incentives and should be amended to authorize such software developers to apply for liability coverage under the Act. Second, through the Wassenaar Arrangement on Export Controls for Conventional Arms and Dual-Use Goods and Technologies, the United States and its international partners should establish uniform controls of dangerous Oday exploit sales targeting critical infrastructure. Third, the United States should amend the Computer Fraud and Abuse Act to strengthen its ability to prosecute researchers located both domestically and abroad who recklessly sell dangerous exploits targeting critical infrastructure to America’s adversaries.
TL;DR: Simulation results over four social network databases from Facebook, Google+, Twitter and YouTube demonstrate the efficiency of the proposed KFCFA algorithm to minimize the information loss of the published data and graph, while satisfying K-anonymity, L-diversity and T-closeness conditions.
Abstract: In recent years, an explosive growth of social networks has been made publicly available for understanding the behavior of users and data mining purposes. The main challenge in sharing the social network databases is protecting public released data from individual identification. The most common privacy preserving technique is anonymizing data by removing or changing some information, while the anonymized data should retain as much information as possible of the original data. K-anonymity and its extensions (e.g., L-diversity and T-closeness) have widely been used for data anonymization. The main drawback of the existing anonymity techniques is the lack of protection against attribute/link disclosure and similarity attacks. Moreover, they suffer from high amount of information loss in the released database. In order to overcome these drawbacks, this paper proposes a combined anonymizing algorithm based on K-member Fuzzy Clustering and Firefly Algorithm (KFCFA) to protect the anonymized database against identity disclosure, attribute disclosure, link disclosure, and similarity attacks, and significantly minimize the information loss. In KFCFA, at first, a modified K-member version of fuzzy c-means is utilized to create balanced clusters with at least K members in each cluster. Then, firefly algorithm is performed for further optimizing the primary clusters and anonymizing the network graph and data. To achieve this purpose, a constrained multi-objective function is introduced to simultaneously minimize the clustering error rate and the generated information loss, while satisfying the defined anonymity constraints. The proposed methodology can be utilized for both network graph structures and micro data. Simulation results over four social network databases from Facebook, Google+, Twitter and YouTube demonstrate the efficiency of the proposed KFCFA algorithm to minimize the information loss of the published data and graph, while satisfying K-anonymity, L-diversity and T-closeness conditions.
TL;DR: Results show that two distinct groups motivated by utilitarian and social objectives respectively drive compulsive use by British students, and this finding highlights the trend of gender agnostic views of social platforms by developers.
Abstract: Positive outcomes of social networking use in both informal and non-educational settings have attracted significant research attention. These benefits include social capital formation, higher job p...
01 Jan 2004
Related Authors (1)
Author's H-index: 5