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Auflistung nach Autor:in "Sheth, Swapneel"

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  • Konferenzbeitrag
    Us and them: A study of privacy requirements across north America, Asia, and Europe
    (Software-engineering and management 2015, 2015) Maalej, Walid; Sheth, Swapneel
    Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. However, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate their privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and their personal data such as location than about their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives.
  • Konferenzbeitrag
    weHelp: A reference architecture for social recommender systems
    (Software Engineering 2010 – Workshopband (inkl. Doktorandensymposium), 2010) Sheth, Swapneel; Arora, Nipun; Murphy, Christian; Kaiser, Gail
    Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems - systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems.