84,965 research outputs found
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
In recent years, it has become easy to obtain location information quite
precisely. However, the acquisition of such information has risks such as
individual identification and leakage of sensitive information, so it is
necessary to protect the privacy of location information. For this purpose,
people should know their location privacy preferences, that is, whether or not
he/she can release location information at each place and time. However, it is
not easy for each user to make such decisions and it is troublesome to set the
privacy preference at each time. Therefore, we propose a method to recommend
location privacy preferences for decision making. Comparing to existing method,
our method can improve the accuracy of recommendation by using matrix
factorization and preserve privacy strictly by local differential privacy,
whereas the existing method does not achieve formal privacy guarantee. In
addition, we found the best granularity of a location privacy preference, that
is, how to express the information in location privacy protection. To evaluate
and verify the utility of our method, we have integrated two existing datasets
to create a rich information in term of user number. From the results of the
evaluation using this dataset, we confirmed that our method can predict
location privacy preferences accurately and that it provides a suitable method
to define the location privacy preference
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Generic system architecture for context-aware, distributed recommendation
In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin
Design and Implementation of S-MARKS: A Secure Middleware for Pervasive Computing Applications
As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module
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Predicted Adoption Rates of Contact Tracing App Configurations - Insights from a choice-based conjoint study with a representative sample of the UK population
Widespread adoption of a contact tracing app by the UK public is an important part of safely easing or lifting the lockdown. In this context, it is essential to understand how adoption rates are influenced by different configurations of a proposed contact tracing app. There are many implementation options that can impact app adoption. For example, which institution should be responsible for and have oversight of the app? What type of data is collected? Does it matter how long it is stored? This whitepaper provides data-driven insights into these and other questions to guide app implementation choices
Context-awareness in mobile tourist information systems: challenges for user interaction
Context in mobile tourist information systems is typically captured as the current location of the user. Few systems consider the user's interests or wider context of the sights. This paper explores ideas of how to model, observe, evaluate, and exploit a richer notion of context in this application area. We discuss the influence of such a richer context model on the user interaction for both the capturing of context and the context-aware user/device interactions
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