15,173 research outputs found
The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service
Department of Management EngineeringAs communication and information technologies advance, the Internet of Things (IoT) has changed the way people live. In particular, as smart home security services have been widely commercialized, it is necessary to examine consumer perception. However, there is little research that explains the general perception of IoT and smart home services. This article will utilize communication privacy management theory and privacy calculus theory to investigate how options to protect privacy affect how users perceive benefits and costs and how those perceptions affect individuals??? intentions to use of smart home service. Scenario-based experiments were conducted, and perceived benefits and costs were treated as formative second-order constructs. The results of PLS analysis in the study showed that smart home options to protect privacy decreased perceived benefits and increased perceived costs. In addition, the perceived benefits and perceived costs significantly affected the intention to use smart home security services. This research contributes to the field of IoT and smart home research and gives practitioners notable guidelines.ope
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems
The use of mobile devices and the rapid growth of the internet and networking
infrastructure has brought the necessity of using Ubiquitous recommender
systems. However in mobile devices there are different factors that need to be
considered in order to get more useful recommendations and increase the quality
of the user experience. This paper gives an overview of the factors related to
the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com
Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer
Science Volume 8530, 2014, pp 369-37
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
Social Transparency through Recommendation Engines and its Challenges: Looking Beyond Privacy
Our knowledge society is quickly becoming a ‘transparent’ one. This transparency is acquired, among other means, by ’personalization’ or ‘profiling’: ICT tools gathering contextualized information about individuals in men–computers interactions. The paper begins with an overview of these ICT tools (behavioral targeting, recommendation engines, ‘personalization’ through social networking). Based on these developments the analysis focus a case study of developments in social network (Facebook) and the trade-offs between ‘personalization’ and privacy constrains. A deeper analysis will reveal unexpected challenges and the need to overcome the privacy paradigm. Finally a draft of possible normative solutions will be depicted, grounded in new forms of individual rights.Recommendation Engines, Profiling, Privacy, ‘Sui Generis’ Copyright
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
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
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