GPERF : a perfect hash function generator

gperf is a widely available perfect hash function generator written in C++. It automates a common system software operation: keyword recognition. gperf translates an n element user-specified keyword list keyfile into source code containing a k element lookup table and a pair of functions, phash and in_word_set. phash uniquely maps keywords in keyfile onto the range 0 .. k - 1, where k >/= n. If k = n, then phash is considered a minimal perfect hash function. in_word_set uses phash to determine whether a particular string of characters str occurs in the keyfile, using at most one string comparison.This paper describes the user-interface, options, features, algorithm design and implementation strategies incorporated in gperf. It also presents the results from an empirical comparison between gperf-generated recognizers and other popular techniques for reserved word lookup

Hashing with binary autoencoders

An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approaches approximate the optimization by relaxing the constraints and then binarizing the result. Here, we focus on the binary autoencoder model, which seeks to reconstruct an image from the binary code produced by the hash function. We show that the optimization can be simplified with the method of auxiliary coordinates. This reformulates the optimization as alternating two easier steps: one that learns the encoder and decoder separately, and one that optimizes the code for each image. Image retrieval experiments, using precision/recall and a measure of code utilization, show the resulting hash function outperforms or is competitive with state-of-the-art methods for binary hashing.Comment: 22 pages, 11 figure

Key recycling in authentication

In their seminal work on authentication, Wegman and Carter propose that to authenticate multiple messages, it is sufficient to reuse the same hash function as long as each tag is encrypted with a one-time pad. They argue that because the one-time pad is perfectly hiding, the hash function used remains completely unknown to the adversary. Since their proof is not composable, we revisit it using a composable security framework. It turns out that the above argument is insufficient: if the adversary learns whether a corrupted message was accepted or rejected, information about the hash function is leaked, and after a bounded finite amount of rounds it is completely known. We show however that this leak is very small: Wegman and Carter's protocol is still $\epsilon$-secure, if $\epsilon$-almost strongly universal$_2$ hash functions are used. This implies that the secret key corresponding to the choice of hash function can be reused in the next round of authentication without any additional error than this $\epsilon$. We also show that if the players have a mild form of synchronization, namely that the receiver knows when a message should be received, the key can be recycled for any arbitrary task, not only new rounds of authentication.Comment: 17+3 pages. 11 figures. v3: Rewritten with AC instead of UC. Extended the main result to both synchronous and asynchronous networks. Matches published version up to layout and updated references. v2: updated introduction and reference

Linear Hashing is Awesome

We consider the hash function $h(x) = ((ax+b) \bmod p) \bmod n$ where $a,b$ are chosen uniformly at random from $\{0,1,\ldots,p-1\}$. We prove that when we use $h(x)$ in hashing with chaining to insert $n$ elements into a table of size $n$ the expected length of the longest chain is $\tilde{O}\!\left(n^{1/3}\right)$. The proof also generalises to give the same bound when we use the multiply-shift hash function by Dietzfelbinger et al. [Journal of Algorithms 1997].Comment: A preliminary version appeared at FOCS'1

Grayscale Image Authentication using Neural Hashing

Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates hash values using neural network one-way property and non-linear techniques. As a result security and performance analysis are performed and satisfying results are achieved. These features are dominant reasons for preferring against traditional ones.Comment: international journal of Natural and Engineering Sciences (NESciences.com) : Image Authentication, Cryptology, Hash Function, Statistical and Security Analysi