A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

An integrative model of the management of hospital physician relationships

Hospital Physician Relationships (HPRs) are of major importance to the health care sector. Drawing on agency theory and social exchange theory, we argue that both economic and noneconomic integration strategies are important to effective management of HPRs. We developed a model of related antecedents and outcomes and conducted a systematic review to assess the evidence base of both integration strategies and their interplay. We found that more emphasis should be placed on financial risk sharing, trust and physician organizational commitment

Stochastic Privacy

Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to enhance the quality of service via personalization of content and to maximize revenues via better targeting of advertisements and deeper engagement of users on sites. To date, service providers have largely followed the approach of either requiring or requesting consent for opting-in to share their data. Users may be willing to share private information in return for better quality of service or for incentives, or in return for assurances about the nature and extend of the logging of data. We introduce \emph{stochastic privacy}, a new approach to privacy centering on a simple concept: A guarantee is provided to users about the upper-bound on the probability that their personal data will be used. Such a probability, which we refer to as \emph{privacy risk}, can be assessed by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on allowable or communicated privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data

Design Model of Application Measurement Imperfect Information to Procesing Data Surveys Level of Website Learning With Fuzzy Query Basis Data Method

Abstract— Mastery of information technology applied in the design of information systems in the form of the web at this time becomes an absolute necessity in implementing business processes of an institution and organization. The level of students 'ability in information systems in web design is a goal to increase students' competitive value in global trading climate. In an effort to increase the mastery of students in designing a web needs to measure the level of mastery, so that the material evaluation of lecturers in the process of teaching and learning activities, especially web courses. Method Fuzzy Query Database is one method to measure the level of imperfect data and information precision (Imperfect Information). In the process of survey level mastery of programming materials and web design data collected not only the exact data but it can be data that contains doubt, imperfection and uncertainty so that in the process of decision-making occurs imperfect information so ineffective and accurate. This research is expected to assist computer lecturers in evaluating the achievement of learning, lecture materials and teaching techniques in the lecture hall

Theory of deferred action: Agent-based simulation model for designing complex adaptive systems

Deferred action is the axiom that agents act in emergent organisation to achieve predetermined goals. Enabling deferred action in designed artificial complex adaptive systems like business organisations and IS is problematical. Emergence is an intractable problem for designers because it cannot be predicted. We develop proof-of-concept, conceptual proto-agent model, of emergent organisation and emergent IS to understand better design principles to enable deferred action as a mechanism for coping with emergence in artefacts. We focus on understanding the effect of emergence when designing artificial complex adaptive systems by developing an exploratory proto-agent model and evaluate its suitability for implementation as agent-based simulation

Linking objective and subjective modeling in engineering design through arc-elastic dominance

Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designers’ subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutions’ feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arc-elasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harrington’s desirability functions and Derringer’s aggregation method. It is carried out through the re-design of a geothermal air conditioning system

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Perspectives on Firm Decision Making During Risky Technology Acquisitions

A novel survey dataset on computed tomography (CT) machine acquisition is used to explore which theories best answer two questions from the decision making literature. First, what determines how much uncertainty a firm has when investing in updated technology? Second, what determines the value of the acquisition? In answering these questions, two theoretical comparisons are conducted. In the first, economic theory, behavioral theory (the Behavioral Theory of the Firm and Prospect Theory), and Bounded Rationality are tested as potential determinants of acquisition uncertainties. In the second, economic theory and Prospect Theory are tested as potential determinants of the value of the machine acquired. To answer these questions, hospitals were surveyed about the acquisition of their most valuable computed tomography machine. From the survey data, support was found for the Bounded Rationality hypothesis; firms have less uncertainty about an acquisition’s performance on attributes that correspond to more strongly held objectives. Support was also found for the behavioral theory hypothesis; firms whose prior machines perform below aspiration levels seek more uncertainty in their subsequent acquisitions, while firms whose machines perform above aspiration levels seek less uncertainty. No support was found for the normative hypothesis; acquisition uncertainty is determined by economic attributes. In the second comparison, partial support was found for the normative theory hypothesis and no support was found for Prospect Theory hypothesis. The value of the acquisition increased as the minimum lifespan of the acquisition increased. Perceived revenue, operating cost, and financial factor uncertainty did not significantly influence acquisition value, providing no support for Prospect Theory. However, greater uncertainty over the acquisition’s ability to fulfill customer desires was associated with the acquisition of a less expensive machine. Studies of the influence of uncertainty on capital investment decision making have traditionally focused on financial forms of uncertainty. The results of this study suggest that the influence of uncertainty related to an acquisition’s ability to fulfill customer desires may have an even stronger influence on the value of an acquisition than variables related to the non-perceptual characteristics of the acquirer