The Invisible Risk: The Data-sharing Activities of Data Brokers and Information Leakage

Data brokers are the major players in the market for collecting, selling and sharing user information. This paper considers data brokers’ data sharing activities as a co-opetition between data brokers and investigates how the information collecting and sharing activities may lead to information leakage on the dark web. We find that S&P 1,500 firms experience higher information leakage when sharing more customer information with data brokers through third-party cookies. Further, using all the registered data brokers and their competitors as the sample, we observe that registered data brokers are more susceptible to information leakage with data sharing activities than unregistered data brokers. Our study provides initial evidence on the consequences of data brokers’ data sharing activities

Social Media Use Purposes and Psychological Wellbeing in Times of Crises

This study investigates the effect of social media (SM) use purposes and user characteristics on individual psychological wellbeing (PWB) during the coronavirus pandemic (COVID-19). Informed by the uses and gratifications theory and PWB research, this study analyzed survey data collected from 282 SM users aged 18 through 59 from a minority-serving university in the United States in March-April 2020. Our quantitative data analysis showed that social media can be used to improve the quality of personal experiences during the COVID-19 crisis through three mechanisms—connectedness (i.e., social), engagement (i.e., collaborative), and entertainment (i.e., hedonic). However, the effect varied by gender, SM usage level, and individual concern about COVID-19 risk. The findings contribute to the literature and offer implications in technology use for enhancing public mental health during crises

Modern DRAM Memory Systems: Performance Analysis and Scheduling Algorithm

The performance characteristics of modern DRAM memory systems are impacted by two primary attributes: device datarate and row cycle time. Modern DRAM device datarates and row cycle times are scaling at different rates with each successive generation of DRAM devices. As a result, the performance characteristics of modern DRAM memory systems are becoming more difficult to evaluate at the same time that they are increasingly limiting the performance of modern computer systems. In this work, a performance evaluation framework that enables abstract performance analysis of DRAM memory systems is presented. The performance evaluation framework enables the performance characterization of memory systems while fully accounting for the effects of datarates, row cycle times, protocol overheads, device power constraints, and memory system organizations. This dissertation utilizes the described evaluation framework to examine the performance impact of the number of banks per DRAM device, the effects of relatively static DRAM row cycle times and increasing DRAM device datarates, power limitation constraints, and data burst lengths in future generations of DRAM devices. Simulation results obtained in the analysis provide insights into DRAM memory system performance characteristics including, but not limited to the following observations. The performance benefit of having a 16 banks over 8 banks increases with increasing datarate. The average performance benefit reaches 18% at 1 Gbps for both open-page and close-page systems. Close-page systems are greatly limited by DRAM device power constraints, while open-page systems are less sensitive to DRAM device power constraints. Increasing burst lengths of future DRAM devices can adversely impact cache-limited processors despite the increasing bandwidth. Performance losses of greater than 50% are observed. Finally, This dissertation also present a unique rank hopping DRAM command-scheduling algorithm designed to alleviate the bandwidth constraints in DDR2 and future DDRx SDRAM memory systems. The proposed rank hopping scheduling algorithm schedules DRAM transactions and command sequences to avoid the power limiting constraints and amortizes the rank-to-rank switching overhead. Execution based simulations show that some workloads are able to fully utilize the additional bandwidth and significant performance improvements are observed across a range of workloads

Talk too much? The Impact of Cybersecurity Disclosures on Investment Decisions

High-profile cybersecurity breaches have raised concerns regarding how organizations disclose security management information to the public. The American Institute of Certified Public Accountants (AICPA) developed a cybersecurity risk management (CSRM) reporting framework to better help organizations convey their cybersecurity programs to the public. In this article, we attempt to provide evidence of how cybersecurity disclosures, as developed by AICPA, affect investment decisions. Our findings suggest that nonprofessional investors are less likely to invest in breached firms with the disclosure of CSRM reports alone. Disclosing the risk management report with an independent assurance report does not result in the mitigation of the negative impact of security breach news. We discuss the corresponding implications

Firm’s Response Strategies after Data Breach and Stock Market Reactions

Recent high-profile data breaches have attracted the public’s and regulators’ attention. Despite the compliance requirement of disclosing data breaches, it is not clear how breached firms respond to the public after data breaches as part of the firm’s overall disaster recovery and public perception management strategies. In this work-in-progress, using data breach data from 2005 to 2018, we investigate how breached firms respond to the public through press releases and how such responses may affect subsequent stock market reactions. Initial descriptive information is provided, and we conclude with future plans

Human vs machine: Do customer service chatbots perform better?

This study aimed to explore and compare the performance of chatbots and human agents on different tasks in online customer services. With practical Twitter data from several customer service accounts, we categorized the users’ inquiries into three categories: uncertain, complex, and ambiguous, and manually evaluated the responding posts in metrics of appropriateness, helpfulness, and empathy. Our results show that, although human agents outperformed chatbots in most tasks under different metrics, chatbots had similar performance in providing helpful responses to uncertain inquiries with a shorter response time. These results indicate that chatbots have relative advantages in responding to uncertain tasks. The findings of our study provide practical implications for chatbot design that, to minimize total transaction costs, organizations may want to consider including a module to redirect uncertain inquiries to human agents after identifying the type of the inquiry