Daniel Schonberg

On compressing encrypted data

When it is desired to transmit redundant data over an insecure and bandwidth-constrained channel, it is customary to first compress the data and then encrypt it. In this paper, we investigate the novelty of reversing the order of these steps, i.e., first encrypting and then compressing, without compromising either the compression efficiency or the information-theoretic security. Although counter-intuitive, we show surprisingly that, through the use of coding with side information principles, this reversal of order is indeed possible in some settings of interest without loss of either optimal coding efficiency or perfect secrecy. We show that in certain scenarios our scheme requires no more randomness in the encryption key than the conventional system where compression precedes encryption. In addition to proving the theoretical feasibility of this reversal of operations, we also describe a system which implements compression of encrypted data.

Toward Compression of Encrypted Images and Video Sequences

We present a framework for compressing encrypted media, such as images and videos. Encryption masks the source, rendering traditional compression algorithms ineffective. By conceiving of the problem as one of distributed source coding, it has been shown in prior work that encrypted data are as compressible as unencrypted data. However, there are two major challenges to realize these theoretical results. The first is the development of models that capture the underlying statistical structure and are compatible with our framework. The second is that since the source is masked by encryption, the compressor does not know what rate to target. We tackle these issues in this paper. We first develop statistical models for images before extending it to videos, where our techniques really gain traction. As an illustration, we compare our results to a state-of-the-art motion-compensated lossless video encoder that requires unencrypted video input. The latter compresses each unencrypted frame of the ldquoForemanrdquo test sequence by 59% on average. In comparison, our proof-of-concept implementation, working on encrypted data, compresses the same sequence by 33%. Next, we develop and present an adaptive protocol for universal compression and show that it converges to the entropy rate. Finally, we demonstrate a complete implementation for encrypted video.

On Compression of Encrypted Images

Coding schemes for secure and efficient communication over noiseless public channels traditionally compress and then encrypt the source data. In some cases reversing the ordering of compression and encryption would be useful, e.g., in enabling the efficient distribution of protected media content. Indeed, not only is it possible to reverse the order, but under some conditions neither security nor compression efficiency need be sacrificed. In earlier work on this problem we have assumed that the source data is either memoryless or has a 1-D Markov structure. Such models are poor matches for the 2-D structure of images. In this work, we use a 2-D source model, and develop a scheme to compress encrypted images based on LDPC codes. We present practical simulation results for compressing bi-level images. In tests, we are able to compress an encrypted 10, 000 bit bi-level image to 4, 299 bits and successfully recover the image exactly. In previous works, the best analogous 1-D model (operating on a raster scanned data sequence of the same source) could only compress the image to 7, 710 bits.

Distributed code constructions for the entire Slepian-Wolf rate region for arbitrarily correlated sources

The Slepian-Wolf coding problem tackles the problem of distributed encoding of correlated discrete-alphabet sources for decoding at a common receiver. In this work, we propose distributed linear block code constructions for attaining any point on the Slepian-Wolf achievable rate region for arbitrarily correlated sources. Specifically, our prescription allows for any arbitrary memoryless joint probability distribution over any arbitrary number of distributed sources, and allows for any arbitrary rate combination that lies in the Slepian-Wolf achievable region. Special cases of our framework include the single source case (wherein our construction reduces to an entropy coder), source coding with side-information at the receiver (so-called corner points of the Slepian-Wolf region), and specific source correlation models (such as induced by a virtual binary symmetric channel model). In this work, we describe how to use low density parity check (LDPC) codes into the proposed framework to solve the general Slepian-Wolf problem constructively.

Iris compression for cryptographically secure person identification

This paper proposes EyeCerts, a biometric system for identification of people, which achieves off-line verification of certified, cryptographically secure documents. An EyeCert is a printed document, which certifies the association of a given text with a biometric feature-a compressed version of a human iris in this work. As a central component of the EyeCert system, an iris analysis technique that extracts and compresses the unique features of a given iris using limited storage is presented. The compressed features should be at maximal distance with respect to a reference iris image database. The iris analysis algorithm performs several steps in three main phases: (i) it detects the human iris, (ii) it converts the isolated iris using a modified Fourier-Mellin transform into a standard domain where the common radial patterns of the human iris are concisely represented, and (iii) it optimally selects, aligns, and near-optimally compresses the most distinctive transform coefficients for each individual user. Using a low quality imaging system (sub-US

An analysis of empirical PMF based tests for least significant bit image steganography

100) and developed and readily available low complexity processing techniques, the overall system is shown to have probabilities of false negative and false positive on the order of 10/sup -5/.

LDPC codes can approach the Slepian Wolf bound for general binary sources

We consider here the class of probability mass-function (PMF) based detectors of least significant bit (LSB) embedded steganography. That is, in this paper we investigate the use of frequency counts of pixel intensities as a statistic for tests detecting the presence of hidden messages. We focus on LSB replacement (though we briefly consider LSB matching) embedding as it is a simple technique where the effect on the true PMF of the resulting image can be understood mathematically. We begin our study by considering the existing tests of Westfeld and Pfitzmann [11] and Dabeer et al.[1]. These tests assume that pixel intensities are random values that are independent and identically distributed (i.i.d.). We generalize these tests by considering PMFs of neighboring pixel intensities. We argue that consideration of higher order of correlations provide only diminishing marginal returns, and thus we can make general statements on the value of PMF based detectors. We measure the performance of our tests by calculation of receiver operating curves (ROC) over a corpus of 350 digital images. We then proceed to compare to a non-PMF based test, in particular the RS tests of Fridrich et al [3]. Although our generalized tests outperform existing PMF based predecessors, they are outperformed by the RS tests. This indicates that using PMFs as a statistic for detecting hidden messages is inherently insufficient.