22 research outputs found
PrivacyCanary: Privacy-aware recommenders with adaptive input obfuscation
Abstract—Recommender systems are widely used by online retailers to promote products and content that are most likely to be of interest to a specific customer. In such systems, users often implicitly or explicitly rate products they have consumed, and some form of collaborative filtering is used to find other users with similar tastes to whom the products can be recommended. While users can benefit from more targeted and relevant recom-mendations, they are also exposed to greater risks of privacy loss, which can lead to undesirable financial and social consequences. The use of obfuscation techniques to preserve the privacy of user ratings is well studied in the literature. However, works on obfuscation typically assume that all users uniformly apply the same level of obfuscation. In a heterogeneous environment, in which users adopt different levels of obfuscation based on their comfort level, the different levels of obfuscation may impact the users in the system in a different way. In this work we consider such a situation and make the following contributions: (a) using an offline dataset, we evaluate the privacy-utility trade-off in a system where a varying portion of users adopt the privacy preserving technique. Our study highlights the effects that each user’s choices have, not only on their own experience but also on the utility that other users will gain from the system; and (b) we propose PrivacyCanary, an interactive system that enables users to directly control the privacy-utility trade-off of the recommender system to achieve a desired accuracy while maximizing privacy protection, by probing the system via a private (i.e., undisclosed to the system) set of items. We evaluate the performance of our system with an off-line recommendations dataset, and show its effectiveness in balancing a target recommender accuracy with user privacy, compared to approaches that focus on a fixed privacy level. I
BuSCOPE: Fusing individual & aggregated mobility behavior for “Live” smart city services
While analysis of urban commuting data has a long and demonstrated history of
providing useful insights into human mobility behavior, such analysis has been
performed largely in offline fashion and to aid medium-to-long term urban
planning. In this work, we demonstrate the power of applying predictive
analytics on real-time mobility data, specifically the smart-card generated
trip data of millions of public bus commuters in Singapore, to create two novel
and "live" smart city services. The key analytical novelty in our work lies in
combining two aspects of urban mobility: (a) conformity: which reflects the
predictability in the aggregated flow of commuters along bus routes, and (b)
regularity: which captures the repeated trip patterns of each individual
commuter. We demonstrate that the fusion of these two measures of behavior can
be performed at city-scale using our BuScope platform, and can be used to
create two innovative smart city applications. The Last-Mile Demand Generator
provides O(mins) lookahead into the number of disembarking passengers at
neighborhood bus stops; it achieves over 85% accuracy in predicting such
disembarkations by an ingenious combination of individual-level regularity with
aggregate-level conformity. By moving driverless vehicles proactively to match
this predicted demand, we can reduce wait times for disembarking passengers by
over 75%. Independently, the Neighborhood Event Detector uses outlier measures
of currently operating buses to detect and spatiotemporally localize dynamic
urban events, as much as 1.5 hours in advance, with a localization error of 450
meters.Comment: ACM MobiSys 201
Privacy in context-aware mobile crowdsourcing systems
National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiativ
Obfuscation at-source: Privacy in context-aware mobile crowd-sourcing
National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ
Scalable urban mobile crowdsourcing: Handling uncertainty in worker movement
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ