9,780 research outputs found
Knowing Your Population: Privacy-Sensitive Mining of Massive Data
Location and mobility patterns of individuals are important to environmental
planning, societal resilience, public health, and a host of commercial
applications. Mining telecommunication traffic and transactions data for such
purposes is controversial, in particular raising issues of privacy. However,
our hypothesis is that privacy-sensitive uses are possible and often beneficial
enough to warrant considerable research and development efforts. Our work
contends that peoples behavior can yield patterns of both significant
commercial, and research, value. For such purposes, methods and algorithms for
mining telecommunication data to extract commonly used routes and locations,
articulated through time-geographical constructs, are described in a case study
within the area of transportation planning and analysis. From the outset, these
were designed to balance the privacy of subscribers and the added value of
mobility patterns derived from their mobile communication traffic and
transactions data. Our work directly contrasts the current, commonly held
notion that value can only be added to services by directly monitoring the
behavior of individuals, such as in current attempts at location-based
services. We position our work within relevant legal frameworks for privacy and
data protection, and show that our methods comply with such requirements and
also follow best-practice
Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
Personalized recommender systems rely on each user's personal usage data in
the system, in order to assist in decision making. However, privacy policies
protecting users' rights prevent these highly personal data from being publicly
available to a wider researcher audience. In this work, we propose a memory
biased random walk model on multilayer sequence network, as a generator of
synthetic sequential data for recommender systems. We demonstrate the
applicability of the synthetic data in training recommender system models for
cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape
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