21,196 research outputs found
Fighting Authorship Linkability with Crowdsourcing
Massive amounts of contributed content -- including traditional literature,
blogs, music, videos, reviews and tweets -- are available on the Internet
today, with authors numbering in many millions. Textual information, such as
product or service reviews, is an important and increasingly popular type of
content that is being used as a foundation of many trendy community-based
reviewing sites, such as TripAdvisor and Yelp. Some recent results have shown
that, due partly to their specialized/topical nature, sets of reviews authored
by the same person are readily linkable based on simple stylometric features.
In practice, this means that individuals who author more than a few reviews
under different accounts (whether within one site or across multiple sites) can
be linked, which represents a significant loss of privacy.
In this paper, we start by showing that the problem is actually worse than
previously believed. We then explore ways to mitigate authorship linkability in
community-based reviewing. We first attempt to harness the global power of
crowdsourcing by engaging random strangers into the process of re-writing
reviews. As our empirical results (obtained from Amazon Mechanical Turk)
clearly demonstrate, crowdsourcing yields impressively sensible reviews that
reflect sufficiently different stylometric characteristics such that prior
stylometric linkability techniques become largely ineffective. We also consider
using machine translation to automatically re-write reviews. Contrary to what
was previously believed, our results show that translation decreases authorship
linkability as the number of intermediate languages grows. Finally, we explore
the combination of crowdsourcing and machine translation and report on the
results
Artificial intelligence and UK national security: Policy considerations
RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security.
The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data
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