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The Role of Privacy in the Intention to Disclose Personal Driving Data Using Internet-of-Vehicle Technology in Consumer Automobiles
Get PDFThe rise of network connectivity has spurred the growth of Internet connected devices. In the modern era of technology, manufacturers are using Internet connectivity to advance their product offerings and development. These development efforts have allowed manufacturers to offer new features to existing products. Many car buyers seem to be enticed by the new features that are possible with this technology, such as advanced infotainment systems, real time data, and instant software updates and upgrades. Internet of Vehicle (IoV) technology is an area of Information Systems (IS) that is rapidly expanding. This technology provides the ability for vehicles and drivers to create data that has not previously existed. Novel IoV data includes aspects of the user’s driving habits, such as speed, acceleration, braking, and location, as well as video and voice recordings from inside the vehicle. As with all systems that collect personal data, privacy is a concern that directly impacts a user’s interest and willingness to adopt a new technology. New automobiles collect IoV data by default with little to no power on the part of the driver to opt out of collection. A better understanding needs to be established regarding how privacy would impact drivers’ decisions to share their personal driving data if given the choice. Previous studies in IoV have focused on car infotainment systems, telematic devices used by insurance companies, and data collection in autonomous vehicles. Whereas a few studies have explored the relationship between privacy concerns and a user’s willingness to share personal driving data, no studies have adequately applied the privacy paradox and privacy calculus theories to their models. These theories are established privacy theories that, when used together, provide a robust and dynamic understanding of privacy attitudes. Further, no study has explored how the perception that a driver has about their own driving ability moderates the relationship between their perception of privacy risk in IoV technology and their intention to disclose personal driving data. This study sought to better understand the role that privacy plays in the user’s decision to disclose personal driving data while using IoV technology in automobiles. This was accomplished by applying a dual calculus model adapted from IoT privacy research based in wearable technology. I quantitative survey was created and administered using an online panel of future vehicle buyers. Data from one hundred and seventy-seven respondents was analyzed through PLS-SEM analysis using SmartPLS4 to test both the theoretical model and the hypotheses. This research found privacy calculus present in the decision new automobile owners would make in sharing their personal driving data with manufacturers if they were given the option. Conversely, the privacy paradox was not found to have a presence in the aforementioned relationship. The results from this study have theoretical implications on the understanding of drivers’ attitudes toward sharing personal driving data associated with established IS constructs incorporating well-known and tested privacy theories. Theoretical contributions also include the exploration of moderating variables on privacy and other IS constructs in the areas of driver ability, driver safety, and intention to share personal driving data. Practical contributions from this study offer insights for the automobile manufacturing industry and government regulatory bodies into how customers would handle personal data disclosure if they were given the option to choose
Safeguarding Virtual Healthcare: A Novel Attacker-Centric Model for Data Security and Privacy
Get PDFThe rapid growth of remote healthcare delivery has introduced significant security and privacy risks to protected health information (PHI). Analysis of a comprehensive healthcare security breach dataset covering 2009-2023 reveals their significant prevalence and impact. This study investigates the root causes of such security incidents and introduces the Attacker-Centric Approach (ACA), a novel threat model tailored to protect PHI. ACA addresses limitations in existing threat models and regulatory frameworks by adopting a holistic attacker-focused perspective, examining threats from the viewpoint of cyber adversaries, their motivations, tactics, and potential attack vectors. Leveraging established risk management frameworks, ACA provides a multi-layered approach to threat identification, risk assessment, and proactive mitigation strategies. A comprehensive threat library classifies physical, third-party, external, and internal threats. ACA\u27s iterative nature and feedback mechanisms enable continuous adaptation to emerging threats, ensuring sustained effectiveness. ACA allows healthcare providers to proactively identify and mitigate vulnerabilities, fostering trust and supporting the secure adoption of virtual care technologies
Analyzing American Voters’ Positive and Negative Responses to AI-Generated Political Advertisements
Get PDFAI-generated political ads have entered the public sphere, but what responses do these ads get from American voters? If voters respond positively to the ads, those could be a viable path political campaigns can venture down. To determine voter responses, a survey will be delivered to a representative sample of the American voting population across age groups, gender, and political leaning, among other areas. Research showed that voters responded negatively to advertisements containing AI-generated imagery overall and were able to identify AI-generated images. They felt more negatively about advertisements when they believed the advertisements contained AI-generated imagery before their suspicions were confirmed. After learning the advertisements contained AI-generated imagery, most respondents reported feeling more negative toward the advertisements in general. Further research is needed to show more detail about the differences in why respondents felt positive or negative towards an advertisement and whether that also affects other variables
Bone Surface Modification Dataset Synthesis for Computer Vision Models Using LoRA Tuned Latent Diffusion Models
Get PDFThis research leveraged generative artificial intelligence to significantly enhance the training dataset of bone surface modification examples, optimizing computer vision classification for archaeological artifacts. The current method of manually producing bone surface modification examples is both time-consuming and labor-intensive, imposing constraints on the number of images available for training computer vision models, which require substantial data to achieve high accuracy. To address this limitation, a generative diffusion model fine-tuned using Low-Rank Adaptation (LoRA) was trained on an existing dataset of bone surface modification examples. The fine-tuned model was then used to generate synthetic images, which were subsequently applied in a computer vision system to identify and classify archaeological artifacts. Specifically, the system determined whether bone surface modifications were made on bones containing meat or those without. Multiple computer vision models were trained and evaluated using both the original dataset and the augmented dataset containing synthetic images. Metrics such as accuracy, loss and F1 scores were assessed, and various combinations of activation functions and optimizers were tested to identify configurations that performed optimally with the synthetic dataset compared to the baseline. The findings demonstrate that synthetic datasets generated through this approach can substantially improve accuracy and F1 scores, particularly in models that perform poorly with smaller, original datasets. Furthermore, the combination of the original dataset with synthetic data amplifies these improvements, solidifying this method as a powerful solution for addressing data scarcity and enhancing the performance of computer vision models in archaeological research
The Psychological Impacts of Algorithmic and AI-Driven Social Media on Teenagers: A Call to Action
Get PDFThis study investigates the meta-issues surrounding social media, which, while theoretically designed to enhance social interactions and improve our social lives by facilitating the sharing of personal experiences and life events, often results in adverse psychological impacts. Our investigation reveals a paradoxical outcome: rather than fostering closer relationships and improving social lives, the algorithms and structures that underlie social media platforms inadvertently contribute to a profound psychological impact on individuals, influencing them in unforeseen ways. This phenomenon is particularly pronounced among teenagers, who are disproportionately affected by curated online personas, peer pressure to present a perfect digital image, and the constant bombardment of notifications and updates that characterize their social media experience. As such, we issue a call to action for policymakers, platform developers, and educators to prioritize the well-being of teenagers in the digital age and work towards creating secure and safe social media platforms that protect the young from harm, online harassment, and exploitation
Machine Learning Security: Threat Model, Attacks, and Challenges
No full textThis article investigates machine learning (ML) security, focusing on threats and attacks against ML. We have created a threat model for ML to illustrate the threat actors involved. We present a new approach to classify threats and attacks targeting ML
Fine-Grained Address Space Layout Randomization Through Non-Contiguous Per-Function Memory Segment Assignment
Get PDFAddress Space Layout Randomization (ASLR) is a popular exploit mitigation provided by most modern operating systems today. ASLR works by randomizing the base address of position-independent code and data segments loaded in memory at run-time in order to make it more difficult for an attacker to guess their locations.
This design choice makes ASLR fast and efficient, but suffers from a major flaw: If an attacker is able to leak any pointer to the randomized memory mapping, they can easily calculate the corresponding base address, and in turn calculate the address of any other code or data in that segment, rendering the protection mechanism entirely useless. In other words, ASLR fails completely in the presence of even a single memory leak vulnerability.
One proposed solution to this problem, commonly referred to as Fine Grained Address Space Layout Randomization (FG-ASLR), is to increase the granularity with which randomization is applied, for instance by randomizing code and data down to the function or basic block level. Many proposals exist, but all suffer from some fatal flaw impacting overall security improvement, load-time performance, run-time performance, memory usage, or disk usage, making them imperfect.
This project proposes a novel FG-ASLR solution utilizing non-contiguous per-function memory segment assignment. This allows code to be randomized at the function level quickly and efficiently, leveraging existing operating system mechanisms, enhancing security without significantly impacting system performance
A Cross-Architecture Framework for Anti-Debugging Techniques
Get PDFAnti-debugging techniques are often used in malware samples and software protection frameworks. From the malware perspective, the malware author includes these checks to hinder the analysis of the sample to increase the time to affect their target. Software protection frameworks on the other hand do this to protect sensitive information such as intellectual property, music, movies, and other proprietary information. The anti-debugging techniques serve to deter reverse engineers from compromising the sensitive data in this case. Modern anti-debugging research focuses on creating new techniques or defeating techniques during analysis. Additionally, modern anti-debugging research is paired with other anti-analysis techniques and used as a component in novel software protection frameworks. This dissertation seeks to better understand anti-debugging techniques in terms of CPU execution time and provide an artifact for anti-debugging identification within target executables based on performance data. This research will use a design science methodology to investigate performance data anti-debugging techniques and generate an artifact that can inform cybersecurity defenders to the presence of anti-debugging techniques based on performance data and statistical tests. The results of this research will be implementations of the anti-debugging techniques, many datasets representing the performance data of Anti-Debugging techniques, statistical results regarding the difference in performance of the anti-debugging techniques compared to a control group, and an artifact that can identify the presence of anti-debugging techniques by measuring the similarity of data variances in performance datasets. In conclusion, the generated artifact was able to detect 27 out of the 58 total techniques across operating systems and architectures with any degree of sensitivity. Out of those 27, seven were significantly less sensitive to changes in execution duration and detected every time in the artifact experiment
Two decades of design research on lessons-learned systems: a systematic review and future research agenda
Get PDFLessons learned systems (LLS) are knowledge management systems (KMS) that collect, verify, store, disseminate, and enable the reuse of experiential knowledge created during the execution of organizational activities. Despite this promise, many scholars have noted that many LLS fail to properly manage their intellectual capital. While behavioral and organizational factors contribute to the success or failure of any IT system, their design is also a critical consideration. Here, a systematic literature review is helpful for understanding the current state of knowledge on LLS design research though to the best of our knowledge, none exists as past reviews have focused on behavioral or organizational factors around the use of KMS. Towards this knowledge gap and to guide future research, we performed a systematic literature review of LLS design research across two decades, finding that most designs employ web technologies, are largely designed for use by managerially- and technically oriented users in the construction context, and only support explicit and declarative knowledge. Based on these findings, we recommend that future research be directed at improving the projectability of design knowledge, supporting the transfer of LL across communities, and improving support for tacit and procedural knowledge
Defending the security of the mind: A mail survey of thought-reform literacy in South Dakota
Get PDFThought reform – also known as brainwashing or psychological manipulation – is a profound threat to the security of the human mind. This complex form of social engineering is associated with terrorist movements, domestic abuse, destructive cults, and many other forms of totalitarianism present in society (Stein, 2017). Thought reform can result in various harms that range in severity depending on each person’s individual experience (Lalich & Tobias, 2006; Stein, 2017). The work of security and privacy professionals inherently concerns psychological manipulation – thought reform often occurs over the internet (Hassan & Shah, 2019), most data breaches occur due to one or more people being maliciously influenced by attackers (Hadnagy, 2018), and the topics of coercion, psychological manipulation, and undue influence have garnered increased attention from privacy and cybersecurity scholars in recent years due to their central role in perpetuating online privacy harms (Citron & Solove, 2022; Susser et al., 2019). The primary tactic for defending people against all forms of psychological exploitation is education and awareness about scams, fraud, and the ways in which attackers exploit human beings (Hadnagy, 2018).
Scholars have identified increasing education and awareness about thought reform as crucial for mitigating the many threats that this type of scam poses to individuals and society (Atack, 2016; Hassan, 2018; Lalich & Tobias, 2006; Langone, 1993; Lifton, 2019; Singer, 2003; Stein, 2017). Despite this recognition, few research studies have discussed or investigated the level of knowledge people have about this security threat. Thus, the purpose of this research study was to address this research gap by doing the foundational work of collecting and analyzing thought-reform literacy data from a geographically-defined population for the first time. This was achieved by developing the Thought-Reform Literacy Questionnaire for South Dakotan Laypeople (TRLQ-SDL) – a novel survey designed to measure thought-reform knowledge among South Dakotan adults. This was administered as a mail survey in South Dakota and a sample of 147 respondents was collected and analyzed to estimate the status of thought-reform literacy in the entire state population.
This dissertation study investigated the primary research question “What is the degree of thought-reform literacy among adult South Dakotan laypeople?” Six additional research questions investigated possible correlations between literacy scores on the TRLQ-SDL and five demographic predictor variables as well as belief in self-vulnerability to thought reform. The overall weighted mean composite thought-reform literacy score for the full sample was 37.03, or 78.79% correct (out of the maximum score of 47), which rose to 38.89 (82.74% correct) with outlier scores removed. A hierarchical multiple regression analysis was conducted on the five demographic predictor variables and no statistically significant correlation was found between any demographic variable and the composite literacy scores.
A point-biserial correlation analysis found a low to moderate positive correlation between those who were humble about their vulnerability to thought reform and composite literacy scores. A separate regression analysis was conducted to predict the score difference between those who were humble and those who believed they were immune to thought reform; the model predicted that those who were humble scored an average of 1.65 points higher after outliers were removed from the sample. This result was statistically significant. This finding resulted in one of the six null hypotheses tested in this study to be rejected in favor of the alternative hypothesis that there is a positive correlation between respondent humility and knowledge about thought reform as measured by the TRLQ-SDL