A (k, ε)-non-malleable extractor is a function nmExt : {0, 1}×{0, 1} → {0, 1} that takes two inputs, a weak source X ∼ {0, 1} of min-entropy k and an independent uniform seed s ∈ {0, 1}, and outputs a bit nmExt(X, s) that is ε-close to uniform, even given the seed s and the value nmExt(X, s′) for an adversarially chosen seed s′ 6= s. Dodis and Wichs (STOC 2009) showed the existence of (k, ε)-no...

We prove (mostly tight) space lower bounds for “streaming” (or “on-line”) computations of four fundamental combinatorial objects: error-correcting codes, universal hash functions, extractors, and dispersers. Streaming computations for these objects are motivated algorithmically by massive data set applications and complexity-theoretically by pseudorandomness and derandomization for spacebounded...

The paper presents a novel approach to the classification of binary image pixels which is based on mathematical morphology. The proposed method makes use of new operators of class extractors to extract from the original image pixels belonging to pre-defined classes. Classes consist of pixels characterized by particular morphological properties. In the paper, a general scheme of morphological cl...

Universal hashing found a lot of applications in computer science. In cryptography the most important fact about universal families is the so called Leftover Hash Lemma, proved by Impagliazzo, Levin and Luby. In the language of modern cryptography it states that almost universal families are good extractors. In this work we provide a somewhat surprising characterization in the opposite directio...

Fuller et al. (Asiacrypt 2013) studied on computational fuzzy extractors, and showed, as a negative result, that the existence of a computational “secure sketch” implies the existence of an information-theoretically secure sketch with slightly weaker parameters. In this work, we show a similar negative result such that, under some computational assumption, the existence of a computational fuzzy...

It has been demonstrated repeatedly that combining multiple types of image features improves the performance of learning-based classification and regression. However, no tools exist to facilitate the creation of large pools of feature extractors by extended teams of contributors. The MASH project aims at creating such tools. It is organized around the development of a collaborative web platform...

Randomness is crucial to computer science, both in theory and applications. In complexity theory, randomness augments computers to offer more powerful models. In cryptography, randomness is essential for seed generation, where the computational model used is generally probabilistic. However, ideal randomness, which is usually assumed to be available in computer science theory and applications, ...