Virtual Scent: Finding Locations of Interest in Ambient Intelligence Environments

Satō, Hiroshi; Inoue, Takeru; Iwamoto, Hideaki; Takahashi, Noriyuki

doi:10.1109/pdcat.2009.41

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…A technique for privacy-conscious recommendation of venues has been presented by Sato et al [19]. Acquired user interests in terms of categories of visited venues are used to provide recommendations.…”

Section: B Privacy and Recommender Systemsmentioning

confidence: 99%

“…The error grows considering more specific hours (e.g., [06,11] and [12,15]), during which the number of submitted check-ins is relatively small (see Table I). Indeed, in those cases, it is more difficult to recognize the top-k venues, since the introduced noise tends 11] 12,521 [12,15] 26,127 [16,19] 56,468 [20,24] 65,557 [00,24] 231,229 to be of the same order of magnitude of the true value (number of check-ins submitted during those hours), thus reducing the utility of the data. However, when considering time-specific queries, the average error is well below 10% using values of j ≤ 2.…”

Section: ) Number K Of Requested Venuesmentioning

confidence: 99%

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Differentially-private release of check-in data for venue recommendation

Riboni

Bettini

2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Abstract-Recommender systems suggesting venues offer very useful services to people on the move and a great business opportunity for advertisers. These systems suggest venues by matching the current context of the user with the venue features, and consider the popularity of venues, based on the number of visits ("check-ins") that they received. Check-ins may be explicitly communicated by users to geo-social networks, or implicitly derived by analysing location data collected by mobile services. In general, the visibility of explicit check-ins is limited to friends in the social network, while the visibility of implicit check-ins limited to the service provider. Exposing check-ins to unauthorized users is a privacy threat since recurring presence in given locations may reveal political opinions, religious beliefs, or sexual orientation, as well as absence from other locations where the user is supposed to be. Hence, on one side mobile app providers host valuable information that recommender system providers would like to buy and use to improve their systems, and on the other we recognize serious privacy issues in releasing that information. In this paper, we solve this dilemma by providing formal privacy guarantees to users and trusted mobile providers while preserving the utility of check-in information for recommendation purposes. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects against both an untrusted recommender system and its users, willing to infer the venues and sensitive locations visited by other users. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods.

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Section: B Privacy and Recommender Systemsmentioning

confidence: 99%

Section: ) Number K Of Requested Venuesmentioning

confidence: 99%

Differentially-private release of check-in data for venue recommendation

Riboni

Bettini

2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…Estas aplicaciones se suelen denominar sistemas sensibles al contexto (context-aware) [34] o de inteligencia ambiental [25,36]. Las aplicaciones Web móviles son mucho más restrictivas que las aplicaciones nativas desde un punto de vista funcional, sobre todo en lo relacionado con las posibilidades técnicas de comunicación con el ambiente físico.…”

Section: Trabajo Relaccionadounclassified

An intelligent Mobile Web Browser to adapt the mobile web as a function of the physical environment

Espada

García‐Díaz

Crespo

et al. 2015

IEEE Latin Am. Trans.

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Today, millions of users all over the world visit websites using their Smartphones. Mobile devices have several features that may contribute to worsen the user experience when using mobile webs, such as: small displays and virtual keyboards, poor precision to select elements using the finger as cursor. In addition, the mobile Web has a peculiar feature, is not always used in the same place or in the same situations; in many cases environmental factors can adversely affect the user experience while he is using a web application, adverse situations derived from the ambient may be different: noise pollution, an excess of light, not being able to see well the letters while walking, etc. This research presents a novel mobile Web paradigm that allows the adaptation of web applications depending on the environment. The proposed solution introduces a novel Mobile Web Browser based on the principles of the Ambient Intelligence (AmI). ThisWeb browser uses the Smartphone's hardware elements as sensors and microphones to capture the environmental information, which will be intelligently analyzed to detect events and other situations.The identified situations will be communicated to the web application, so that appropriate adaptation are made and there by improve the user experience.

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A Platform for Privacy-Preserving Geo-social Recommendation of Points of Interest

Riboni

Bettini

2013

2013 IEEE 14th International Conference on Mobile Data Management

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Virtual Scent: Finding Locations of Interest in Ambient Intelligence Environments

Cited by 4 publications

References 9 publications

Differentially-private release of check-in data for venue recommendation

Differentially-private release of check-in data for venue recommendation

An intelligent Mobile Web Browser to adapt the mobile web as a function of the physical environment

A Platform for Privacy-Preserving Geo-social Recommendation of Points of Interest

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