17 research outputs found
Towards the Development of a Cyber Analysis & Advisement Tool (CAAT) for Mitigating De-Anonymization Attacks
We are seeing a rise in the number of Anonymous Social Networks (ASN) that claim to provide a sense of user anonymity. However, what many users of ASNs do not know that a person can be identified by their writing style.
In this paper, we provide an overview of a number of author concealment techniques, their impact on the semantic meaning of an author\u27s original text, and introduce AuthorCAAT, an application for mitigating de-anonymization attacks. Our results show that iterative paraphrasing performs the best in terms of author concealment and performs well with respect to Latent Semantic Analysis
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
Style Obfuscation by Invariance
The task of obfuscating writing style using sequence models has previously
been investigated under the framework of obfuscation-by-transfer, where the
input text is explicitly rewritten in another style. These approaches also
often lead to major alterations to the semantic content of the input. In this
work, we propose obfuscation-by-invariance, and investigate to what extent
models trained to be explicitly style-invariant preserve semantics. We evaluate
our architectures on parallel and non-parallel corpora, and compare automatic
and human evaluations on the obfuscated sentences. Our experiments show that
style classifier performance can be reduced to chance level, whilst the
automatic evaluation of the output is seemingly equal to models applying
style-transfer. However, based on human evaluation we demonstrate a trade-off
between the level of obfuscation and the observed quality of the output in
terms of meaning preservation and grammaticality.Comment: Accepted for presentation at COLING1