All Your Queries Are Belong to Us: The Power of File-Injection Attacks on Searchable Encryption

The goal of searchable encryption (SE) is to enable a client to execute searches over encrypted files stored on an untrusted server while ensuring some measure of privacy for both the encrypted files and the search queries. Research has focused on developing efficient SE schemes at the expense of allowing some small, well-characterized (information) leakage to the server about the files and/or the queries. The practical impact of this leakage, however, remains unclear. We thoroughly study file-injection attacks--in which the server sends files to the client that the client then encrypts and stores--on the query privacy of single-keyword and conjunctive SE schemes. We show such attacks can reveal the client\u27s queries in their entirety using very few injected files, even for SE schemes having low leakage. We also demonstrate that natural countermeasures for preventing file-injection attacks can be easily circumvented. Our attacks outperform prior work significantly in terms of their effectiveness as well as in terms of their assumptions about the attacker\u27s prior knowledge

Automatic correction of grammatical errors in non-native English text

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 99-107).Learning a foreign language requires much practice outside of the classroom. Computer-assisted language learning systems can help fill this need, and one desirable capability of such systems is the automatic correction of grammatical errors in texts written by non-native speakers. This dissertation concerns the correction of non-native grammatical errors in English text, and the closely related task of generating test items for language learning, using a combination of statistical and linguistic methods. We show that syntactic analysis enables extraction of more salient features. We address issues concerning robustness in feature extraction from non-native texts; and also design a framework for simultaneous correction of multiple error types. Our proposed methods are applied on some of the most common usage errors, including prepositions, verb forms, and articles. The methods are evaluated on sentences with synthetic and real errors, and in both restricted and open domains. A secondary theme of this dissertation is that of user customization. We perform a detailed analysis on a non-native corpus, illustrating the utility of an error model based on the mother tongue. We study the benefits of adjusting the correction models based on the quality of the input text; and also present novel methods to generate high-quality multiple-choice items that are tailored to the interests of the user.by John Sie Yuen Lee.Ph.D

Text generation from keywords

We describe a method for generating sentences from “keywords ” or “headwords”. This method consists of two main parts, candidate-text con-struction and evaluation. The construction part generates text sentences in the form of depen-dency trees by using complementary informa-tion to replace information that is missing be-cause of a “knowledge gap ” and other missing function words to generate natural text sen-tences based on a particular monolingual cor-pus. The evaluation part consists of a model for generating an appropriate text when given keywords. This model considers not only word n-gram information, but also dependency infor-mation between words. Furthermore, it consid-ers both string information and morphological information.