Fuzzy extractors are a powerful tool to extract randomness from noisy data. A fuzzy extractor can extract randomness only if the source data is discrete while in practice source data is continuous. Using quantizers to transform continuous data into discrete data is a commonly used solution. However, as far as we know no study has been made of the effect of the quantization strategy on the perfo...

The task of data fusion is to identify the true values of data items (e.g., the true date of birth for Tom Cruise) among multiple observed values drawn from different sources (e.g., Web sites) of varying (and unknown) reliability. A recent survey [20] has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of diffe...

The topic of generating random bits from imperfect random sources was studied extensively. Existing work provides solutions that are based on the concept of a fixed-length extractor; namely, an algorithm that gets a fixed number of random bits from a source and generates random bits. Typically, the number of random bits extracted using fixed-length extractors is upperbounded by the min-entropy....

Owing to the popularity of the PDF format and the continued exploitation of Adobe Reader, the detection of malicious PDFs remains a concern. All existing detection techniques rely on the PDF parser to a certain extent, while the complexity of the PDF format leaves an abundant space for parser confusion. To quantify the difference between these parsers and Adobe Reader, we create a reference Jav...

Mobile platforms use biometrics for authentication. Unfortunately, biometrics exhibit noise between repeated readings. Due to the noise, biometrics are stored in plaintext, so device compromise completely reveals the user’s biometric value. To limit privacy violations, one can use fuzzy extractors to derive a stable cryptographic key from biometrics (Dodis et al., Eurocrypt 2004). Unfortunately...