(Georgia Institute of Technology, 2017)
Eisenstein, Jacob
In lexicon-based classification, documents are assigned labels
by comparing the number of words that appear from two opposed
lexicons, such as positive and negative sentiment. Creating
such words lists is often easier than labeling instances,
and they can be debugged by non-experts if classification performance
is unsatisfactory. However, there is little analysis
or justification of this classification heuristic. This paper describes
a set of assumptions that can be used to derive a probabilistic
justification for lexicon-based classification, as well
as an analysis of its expected accuracy. One key assumption
behind lexicon-based classification is that all words in each
lexicon are equally predictive. This is rarely true in practice,
which is why lexicon-based approaches are usually outperformed
by supervised classifiers that learn distinct weights on
each word from labeled instances. This paper shows that it is
possible to learn such weights without labeled data, by leveraging
co-occurrence statistics across the lexicons. This offers
the best of both worlds: light supervision in the form of lexicons,
and data-driven classification with higher accuracy than
traditional word-counting heuristics.
(Georgia Institute of Technology, 2014-07)
Lau, Billy; Chung, Simon; Song, Chengyu; Jang, Yeongjin; Lee, Wenke; Boldyreva, Alexandra
Users are increasingly storing, accessing, and exchanging
data through public cloud services such as
those provided by Google, Facebook, Apple, and Microsoft.
Although users may want to have faith in cloud
providers to provide good security protection, the Snowden
expos´e is the latest reminder of the reality we live in:
the confidentiality of any data in public clouds can be violated,
and consequently, while the providers may not be
“doing evil”, we can not and should not trust them with
data confidentiality.
To better protect the privacy of user data stored on the
cloud, in this paper we propose a privacy-preserving system
called Mimesis Aegis (M-Aegis) that is suitable for
mobile platforms. M-Aegis is a new approach to user
data privacy that not only provides isolation but also preserves
user experience, through the creation of a conceptual
layer called Layer 7.5 (L-7.5), which is interposed
between the application (Layer 7) and the user (Layer 8).
This approach allows M-Aegis to implement a true endto-
end encryption of user data with three goals in mind:
1) complete data and logic isolation from untrusted entities;
2) the preservation of original user experience with
target apps; and 3) applicable to a large number of apps
and resilient to updates.