Browsing by Subject "trust management"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Enhancing Trust-based Data Analytics for Forecasting Social Harm
    (IEEE, 2020-09) Chowdhury, Nahida Sultana; Raje, Rajeev R.; Pandey, Saurabh; Mohler, George; Carter, Jeremy; School of Public and Environmental Affairs
    First responders deal with a variety of “social harm” events (e.g. crime, traffic crashes, medical emergencies) that result in physical, emotional, and/or financial hardships. Through data analytics, resources can be efficiently allocated to increase the impact of interventions aimed at reducing social harm -T-CDASH (Trusted Community Data Analytics for Social Harm) is an ongoing joint effort between the Indiana University Purdue University Indianapolis (IUPUI), the Indianapolis Metropolitan Police Department (IMPD), and the Indianapolis Emergency Medical Services (IEMS) with this goal of using data analytics to efficiently allocate resources to respond to and reduce social harm. In this paper, we make several enhancements to our previously introduced trust estimation framework T-CDASH. These enhancements include additional metrics for measuring the effectiveness of forecasts, evaluation on new datasets, and an incorporation of collaborative trust models. To empirically validate our current work, we ran simulations on newly collected 2019 and 2020 (Jan-April) social harm data from the Indianapolis metro area. We describe the behavior and significance of the collaboration and their comparison with previously introduced stand-alone models.
  • Thumbnail Image
    Item
    A multi-domain trust management model for supporting RFID applications of IoT
    (PLOS, 2017-07-14) Wu, Xu; Li, Feng; Engineering Technology, School of Engineering and Technology
    The use of RFID technology in complex and distributed environments often leads to a multi-domain RFID system, in which trust establishment among entities from heterogeneous domains without past interaction or prior agreed policy, is a challenge. The current trust management mechanisms in the literature do not meet the specific requirements in multi-domain RFID systems. Therefore, this paper analyzes the special challenges on trust management in multi-domain RFID systems, and identifies the implications and the requirements of the challenges on the solutions to the trust management of multi-domain RFID systems. A multi-domain trust management model is proposed, which provides a hierarchical trust management framework include a diversity of trust evaluation and establishment approaches. The simulation results and analysis show that the proposed method has excellent ability to deal with the trust relationships, better security, and higher accuracy rate.
  • Thumbnail Image
    Item
    Trust Estimation of Historical Social Harm Events in Indianapolis Metro Area
    (IEEE, 2019-10) Pandey, Saurabh; Chowdhury, Nahida; Raje, Rajeev R.; Mohler, George; Carter, Jeremy; School of Public and Environmental Affairs
    Social harm involves incidents resulting in physical, financial, and emotional hardships such as crime, drug overdoses and abuses, traffic accidents, and suicides. These incidents require various law-enforcement and emergencyresponding agencies to coordinate together for mitigating their impact on society. In this paper, we discuss the enhancements made to Community Data Analytic for Social Harm Prevention (CDASH) - a system that we have created for analyzing historical social harm events. CDASH predicts `hot-spots’ and displays them graphically to law-enforcement officials. The enhanced system, called Trusted-CDASH (T-CDASH), superimposes a trust estimation framework on top of CDASH. We discuss the importance and necessity of associating a degree of trust with each social harm incident reported to T-CDASH. We also describe different trust models that can be incorporated for assigning trust while examining their impact on prediction accuracy of future social harm events. To validate the trust models, we run simulations on historical social harm data of Indianapolis metro area, illustrating the behavior of each trust model and exploring their significance.
  • Thumbnail Image
    Item
    Using Twitter trust network for stock market analysis
    (Elsevier, 2018-04) Ruan, Yefeng; Durresi, Arjan; Alfantoukh, Lina; Computer and Information Science, School of Science
    Online social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers.