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IIM Indore

Privacy concerns of users threaten the usage of online social networks (OSN). In this regard, privacy preserving of OSN emerged as a convincing solution for preserving the privacy of users and uncovering useful insights from the social network data. In this article, we propose a novel algorithm based on the fuzzy sets and rewiring algorithm for preserving the privacy of users. This article presents the algorithm called privacy-preserving rewiring algorithm (PPRA), which can be used for anonymizing the social network data. The algorithm is validated by showing its effectiveness on four real-world datasets across three major graph mining tasks. The proposed PPRA algorithm will help in preserving the privacy of users in the OSN graph while simultaneously maintaining the utility that can be generated from the OSN graph structure. IEEE

Information Systems

World University root node

Privacy Preserving in Online Social Networks Using Fuzzy Rewiring

Industry 4.0 (I4.0) has gained a lot of consideration as it can address the challenges of today's dynamic manufacturing environment. This paper empirically examines whether environmental dynamism (ED) can drive firms to implement I4.0 technologies, and mediating effect of critical factors on this relationship. We adopt “Human-Organization-Technology” (HOT) theory to categorize the critical factors for I4.0. The paper also examines the effect of I4.0 on environmental and market performance. The proposed framework is tested by partial least squares (PLS) using the survey data from Indian manufacturing industries. The results show that ED drives firms towards I4.0, and positive effect of I4.0 on performance outcomes. Further, organizational and technological factors mediate the relationship between ED and I4.0. The findings can be useful for firms considering to make a transition towards I4.0. It can aid managers to formulate specific strategies for transitioning to I4.0, contribute towards sustainable development, and improve market performance. © 2021 Elsevier Inc.

Industrial Marketing Management

Environmental dynamism, industry 4.0 and performance: Mediating role of organizational and technological factors

This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various stakeholders such as the central bank, government, and investors try to maximize the returns and minimize the risk in their decision-making using exchange rate forecasting. The study aims to propose a novel ensemble technique to forecast daily exchange rates for the three most traded currency pairs (EUR/USD, GBP/USD, and JPY/USD). The ensemble technique combines the linear and non-linear time-series forecasting techniques (mean forecast, ARIMA, and neural network) with their most optimal weights. We have taken the data of more than seven years, and the results indicate that the proposed methodology could be an effective technique to forecast better as compared to the component models separately. The study has crucial economic and academic implications. The results derived from this study would be useful for policymakers, regulators, investors, speculators, and arbitrageurs. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.

Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications

Purpose: The 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/approach: The 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). Findings: 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. The other significant antecedents for enhanced cyber-security in organisations include “role of senior management” and “proactive information security”. Research limitations/implications: This 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/value: The 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. © 2020, Emerald Publishing Limited.

Fulltext

Journal of Enterprise Information Management

Antecedents for enhanced level of cyber-security in organisations

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 &#x201C;Data governance&#x201D; is the most critical factor, as it affects all the four performance outcomes (operational, product, economic, and responsiveness). Similarly, &#x201C;Legal aspects&#x201D; affects three out of the four performance outcomes (operational, product, and economic performance), while &#x201C;Collaboration and teamwork&#x201D; 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. IEEE

Critical Success Factors of Industry 4.0 in Automotive Manufacturing Industry

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 trust, privacy concerns and behavioural intention of users on the social network. The behavioural intention of users on the online social network (OSN) is captured by intention to disclose information and intention to interact with others in OSN. The study was conducted on a sample of 457 active users from one of the major social networking website, Facebook. Partial least squares based structural equation modelling was used for analysing the results. The findings of the study reveal that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others. Another important finding of the study indicates that prior positive experience with the website significantly impacts the trust in website, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.

Behaviour & Information Technology

Interplay between trust, information privacy concerns and behavioural intention of users on online social networks

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.

Expert Systems with Applications

Upper approximation based privacy preserving in online social networks

Privacy preserving graph publishing is gaining importance in recent times mainly because of inherent privacy issues existing in publishing graph and social network data. Therefore, graph structure needs to be anonymized before publishing. This study proposes anonymization of a graph using fuzzy sets to preserve the graph's privacy while maintaining the utility that can be derived from the graph. We have conducted the experiments on four different datasets, and the results suggest that the proposed approach would not only help in protecting the privacy of data but also in maintaining the quality of data for analysis. To check the robustness of the proposed approach, we have validated the effectiveness of the approach on five key community detection algorithms on three performance measures.

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

Privacy preserving graph publishing using fuzzy set

The Indian motion picture industry has experienced phenomenal growth during the last few decades and plays an important role in emerging economy of India. This paper integrates three analytical models in order to address the intriguing problem of revenue prediction of movies in Indian film industry. The paper attempts to investigate the determinants leading to the success of indigenous movies in Indian context. Ensemble model has been constructed by integrating the three analytical models (Neural Network, Classification and Regression Tree and Robust Regression) using linear optimization approach. Further, a four-way comparative analysis of these three models along with Ensemble model has been carried out. The predictive power of the models has been evaluated using four performance metrics namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and large prediction error (LPE). Analyzing novel and original data of 120 Indian movies released during the period August'06-October'15, this paper inspects the nitty-gritties of Indian film industry and seeks to explain the nuances. The study revealed that factors like hype generated on web by a movie, screens on which the movie is released, rating garnered by movie and its genre are the most influential variables in deciding the box-office performance of a movie. Further we observed, that the neural network model closely competes with ensemble model in terms of predictive accuracy. The ensemble model considerably reduces the predictive errors and yields better results on two of the performance metrics.