Statistical analysis of identity risk of exposure and cost using the ecosystem of identity attributes

Personally Identifiable Information (PII) is often called the "currency of the Internet" as identity assets are collected, shared, sold, and used for almost every transaction on the Internet. PII is used for all types of applications from access control to credit score calculations to targeted advertising. Every market sector relies on PII to know and authenticate their customers and their employees. With so many businesses and government agencies relying on PII to make important decisions and so many people being asked to share personal data, it is critical to better understand the fundamentals of identity to protect it and responsibly use it. Previously developed comprehensive Identity Ecosystem utilizes graphs to model PII assets and their relationships and is powered by empirical data from almost 6,000 real-world identity theft and fraud news reports to populate the UT CID Identity Ecosystem. We analyze UT CID Identity Ecosystem using graph theory and report numerous novel statistics using identity asset content, structure, value, accessibility, and impact. Our work sheds light on how identity is used and paves the way for improving identity protection.Electrical and Computer Engineerin

Specialization in the identity ecosystem

textCyberspace has dramatically improved our daily lives in the past several decades. Meanwhile, people’s personal identifiable information (PII) is exposed online and is at risk of identity theft and cybercrimes. The Identity Ecosystem developed by the Center for Identity in the University of Texas at Austin addresses this problem and provides a statistical framework for understanding the value, risk and mutual relationships of PII. The Identity Ecosystem currently uses a general Bayesian Network Model to simulate the relationships among PII, which may be quite inaccurate for specific groups of people. This thesis proposes a solution that specializes the Bayesian Network used for particular groups of people. Both one-dimension specialization and multi-dimension specialization are investigated. Research problems like how to choose specialization criterion, how to set specialization boundaries, and how to overcome the difficult of insufficient data, are carefully studied. Specialization functionality is demonstrated based on empirical data. Finally, experiments of specialization are conducted on data obtained from online stories. This work is important in the sense that it provides a guide-line of designing more accurate models of PII within the Identity Ecosystem.Electrical and Computer Engineerin

Place attachment and preferences for wind energy – A value-based approach

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Predicting homebuyers’ intentions of inhabiting eco-friendly homes: The case of a developing country

Malaysian housing developers are still weighing the costs and benefits of building environmentally sensitive homes as many of them are concerned that there is not enough demand for these homes. The objective of this paper is to examine the relative importance of psychosocial, housing and demographic determinants in influencing intention to inhabit eco-friendly homes. The results indicated that a favorable attitude toward environmentally sensitive homes, high control in the ability to purchase sustainable homes, and the role of identification with green consumerism were statistically significant predictors of intention to inhabit such homes. However, social referents’ opinion relating to green and sustainable homes was not significantly related to the intention of inhabiting. The findings also indicated that owners of gated-guarded and detached dwellings, monthly household income and higher educational attainment were significantly related to the likelihood of residing in eco-friendly homes. Housing developers should have to take the lead to generate awareness of sustainability of green homes through education because increasing awareness creates demand for eco-friendly homes, which would in turn push house buyers to go green

Human experience in the natural and built environment : implications for research policy and practice

22nd IAPS conference. Edited book of abstracts. 427 pp. University of Strathclyde, Sheffield and West of Scotland Publication. ISBN: 978-0-94-764988-3

Using persuasive technology to promote sustainable behavior in smart home environments

Sustainable living is to a large extent the outcome of how consumers use the technology surrounding them. Seen from this perspective the rather strict separation of technological and behavioral solution is not only artificial but also detrimental to finding real sustainable solutions. Persuasive technology aims to intervene in these user-system interactions by using intelligent agents to change human attitudes and behavior. Embodied agents like robots and avatars go beyond the function of a simple tool by adopting social behavior that allows for social influence on human users. In addition intelligent systems can provide experiences that are impossible in the physical reality and which may enable experiences that promote more adequate reactions to future and distant climate risks

Willingness to Pay for Agricultural Environmental Safety: Evidence from a Survey of Milan, Italy, Residents

The widespread use of pesticides in agriculture provides a particularly complex pattern of multidimensional negative side-effects, ranging from food safety related effects to the deterioration of farmland ecosystems. The assessment of the economic implications of such negative processes is fraught with many uncertainties. This paper presents results of an empirical study recently conducted in the North of Italy aimed at estimating the value of reducing the multiple impacts of pesticide use. A statistical technique known as conjoint choice experiment is used here in combination with contingent valuation techniques. The experimental design of choice modelling provides a natural tool to attach a monetary value to negative environmental effects associated with agrochemicals use. In particular, the paper addresses the reduction of farmland biodiversity, groundwater contamination and human intoxication. The resulting estimates show that, on average, respondents are prone to accept substantial willingness to pay premia for agricultural goods (in particular, foodstuff) produced in environmentally benign ways.Pesticide risks, Food safety, Willingness-to-pay, Choice modeling, Contingent valuation

Species traits and geomorphic setting as drivers of global soil carbon stocks in seagrass meadows

Unidad de excelencia María de Maeztu CEX2019-000940-MOur knowledge of the factors that can influence the stock of organic carbon (OC) that is stored in the soil of seagrass meadows is evolving, and several causal effects have been used to explain the variation of stocks observed at local to national scales. To gain a global-scale appreciation of the drivers that cause variation in soil OC stocks, we compiled data on published species-specific traits and OC stocks from monospecific and mixed meadows at multiple geomorphological settings. Species identity was recognized as an influential driver of soil OC stocks, despite their large intraspecific variation. The most important seagrass species traits associated with OC stocks were the number of leaves per seagrass shoot, belowground biomass, leaf lifespan, aboveground biomass, leaf lignin, leaf breaking force and leaf OC plus the coastal geomorphology of the area, particularly for lagoon environments. A revised estimate of the global average soil OC stock to 20 cm depth of 15.4 Mg C ha−1 is lower than previously reported. The largest stocks were still recorded in Mediterranean seagrass meadows. Our results specifically identify Posidonia oceanica from the Mediterranean and, more generally, large and persistent species as key in providing climate regulation services, and as priority species for conservation for this specific ecosystem service