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Profiling user activities with minimal traffic traces
Understanding user behavior is essential to personalize and enrich a user's
online experience. While there are significant benefits to be accrued from the
pursuit of personalized services based on a fine-grained behavioral analysis,
care must be taken to address user privacy concerns. In this paper, we consider
the use of web traces with truncated URLs - each URL is trimmed to only contain
the web domain - for this purpose. While such truncation removes the
fine-grained sensitive information, it also strips the data of many features
that are crucial to the profiling of user activity. We show how to overcome the
severe handicap of lack of crucial features for the purpose of filtering out
the URLs representing a user activity from the noisy network traffic trace
(including advertisement, spam, analytics, webscripts) with high accuracy. This
activity profiling with truncated URLs enables the network operators to provide
personalized services while mitigating privacy concerns by storing and sharing
only truncated traffic traces.
In order to offset the accuracy loss due to truncation, our statistical
methodology leverages specialized features extracted from a group of
consecutive URLs that represent a micro user action like web click, chat reply,
etc., which we call bursts. These bursts, in turn, are detected by a novel
algorithm which is based on our observed characteristics of the inter-arrival
time of HTTP records. We present an extensive experimental evaluation on a real
dataset of mobile web traces, consisting of more than 130 million records,
representing the browsing activities of 10,000 users over a period of 30 days.
Our results show that the proposed methodology achieves around 90% accuracy in
segregating URLs representing user activities from non-representative URLs
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