4,446 research outputs found
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
A Novel Quantum Visual Secret Sharing Scheme
Inspired by Naor et al.'s visual secret sharing (VSS) scheme, a novel n out
of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of
two phases: sharing process and recovering process. In the first process, the
color information of each pixel from the original secret image is encoded into
an n-qubit superposition state by using the strategy of quantum expansion
instead of classical pixel expansion, and then these n qubits are distributed
as shares to n participants, respectively. During the recovering process, all
participants cooperate to collect these n shares of each pixel together, then
perform the corresponding measurement on them, and execute the n-qubit XOR
operation to recover each pixel of the secret image. The proposed scheme has
the advantage of single-pixel parallel processing that is not available in the
existing analogous quantum schemes and perfectly solves the problem that in the
classic VSS schemes the recovered image has the loss in resolution. Moreover,
its experiment implementation with the IBM Q is conducted to demonstrate the
practical feasibility.Comment: 19 pages, 13 figure
Secret Sharing Schemes Based on Error-Correcting Codes
In this thesis we present a new secret sharing scheme based on binary error-correcting
codes, which can realize arbitrary (monotone or non-monotone) access structures.
In this secret sharing scheme the secret is a codeword in a binary error-correcting
code and the shares are binary words of the same length. When a group of participants
wants to reconstruct the secret, the participants calculate the sum of their shares and
apply Hamming decoding to that sum. The shares have the property that, when
the group is authorized, the secret is the codeword which is closest to the sum of the
shares. Otherwise, the sum differs strongly enough from the secret such that Hamming
decoding yields another codeword.
The shares can be described by the solutions of a system of linear equations which
is closely related to first order Reed-Muller codes. We consider the case that there are
only two different Hamming distances from the sums of the shares to the secret: one
small distance k for the authorized sets and one large distance g for unauthorized sets.
For this case a method of how to find suitable shares for arbitrary access structures is
presented.
In the resulting secret sharing scheme large code lengths are needed and the security
distance g is rather small. In order to find classes of access structures which have more
efficient and secure realizations, we classify the access structures such that all access
structures of one class allow the same parameters g and k. Furthermore we study
several changes in the access structure and their impact on the possible realizations.
This gives rise to special classes of access structures defined by veto sets and
necessary sets, which are particularly suitable for our approach
Visual pattern recognition using neural networks
Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks.
In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance.
We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations.
Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results
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