3,794 research outputs found
Cryptanalysis of an Encryption Scheme Based on Blind Source Separation
Recently Lin et al. proposed a method of using the underdetermined BSS (blind
source separation) problem to realize image and speech encryption. In this
paper, we give a cryptanalysis of this BSS-based encryption and point out that
it is not secure against known/chosen-plaintext attack and chosen-ciphertext
attack. In addition, there exist some other security defects: low sensitivity
to part of the key and the plaintext, a ciphertext-only differential attack,
divide-and-conquer (DAC) attack on part of the key. We also discuss the role of
BSS in Lin et al.'s efforts towards cryptographically secure ciphers.Comment: 8 pages, 10 figures, IEEE forma
Nonnegative Matrix Factorization Applied to Nonlinear Speech and Image Cryptosystems
Nonnegative matrix factorization (NMF) is widely used in signal separation and image compression. Motivated by its successful applications, we propose a new cryptosystem based on NMF, where the nonlinear mixing (NLM) model with a strong noise is introduced for encryption and NMF is used for decryption. The security of the cryptosystem relies on following two facts: 1) the constructed multivariable nonlinear function is not invertible; 2) the process of NMF is unilateral, if the inverse matrix of the constructed linear mixing matrix is not nonnegative. Comparing with Lin\u27s method (2006) that is a theoretical scheme using one-time padding in the cryptosystem, our cipher can be used repeatedly for the practical request, i.e., multitme padding is used in our cryptosystem. Also, there is no restriction on statistical characteristics of the ciphers and the plaintexts. Thus, more signals can be processed (successfully encrypted and decrypted), no matter they are correlative, sparse, or Gaussian. Furthermore, instead of the number of zero-crossing-based method that is often unstable in encryption and decryption, an improved method based on the kurtosis of the signals is introduced to solve permutation ambiguities in waveform reconstruction. Simulations are given to illustrate security and availability of our cryptosystem
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Privacy protection in medical data is a legitimate obstacle for centralized
machine learning applications. Here, we propose a client-server image
segmentation system which allows for the analysis of multi-centric medical
images while preserving patient privacy. In this approach, the client protects
the to-be-segmented patient image by mixing it to a reference image. As shown
in our work, it is challenging to separate the image mixture to exact original
content, thus making the data unworkable and unrecognizable for an unauthorized
person. This proxy image is sent to a server for processing. The server then
returns the mixture of segmentation maps, which the client can revert to a
correct target segmentation. Our system has two components: 1) a segmentation
network on the server side which processes the image mixture, and 2) a
segmentation unmixing network which recovers the correct segmentation map from
the segmentation mixture. Furthermore, the whole system is trained end-to-end.
The proposed method is validated on the task of MRI brain segmentation using
images from two different datasets. Results show that the segmentation accuracy
of our method is comparable to a system trained on raw images, and outperforms
other privacy-preserving methods with little computational overhead
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
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