15,449 research outputs found
THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system
In this paper, we propose a new biometric verification and template
protection system which we call the THRIVE system. The system includes novel
enrollment and authentication protocols based on threshold homomorphic
cryptosystem where the private key is shared between a user and the verifier.
In the THRIVE system, only encrypted binary biometric templates are stored in
the database and verification is performed via homomorphically randomized
templates, thus, original templates are never revealed during the
authentication stage. The THRIVE system is designed for the malicious model
where the cheating party may arbitrarily deviate from the protocol
specification. Since threshold homomorphic encryption scheme is used, a
malicious database owner cannot perform decryption on encrypted templates of
the users in the database. Therefore, security of the THRIVE system is enhanced
using a two-factor authentication scheme involving the user's private key and
the biometric data. We prove security and privacy preservation capability of
the proposed system in the simulation-based model with no assumption. The
proposed system is suitable for applications where the user does not want to
reveal her biometrics to the verifier in plain form but she needs to proof her
physical presence by using biometrics. The system can be used with any
biometric modality and biometric feature extraction scheme whose output
templates can be binarized. The overall connection time for the proposed THRIVE
system is estimated to be 336 ms on average for 256-bit biohash vectors on a
desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link
connection speed. Consequently, the proposed system can be efficiently used in
real life applications
Fault-Tolerant Thresholds for Encoded Ancillae with Homogeneous Errors
I describe a procedure for calculating thresholds for quantum computation as
a function of error model given the availability of ancillae prepared in
logical states with independent, identically distributed errors. The thresholds
are determined via a simple counting argument performed on a single qubit of an
infinitely large CSS code. I give concrete examples of thresholds thus
achievable for both Steane and Knill style fault-tolerant implementations and
investigate their relation to threshold estimates in the literature.Comment: 14 pages, 5 figures, 3 tables; v2 minor edits, v3 completely revised,
submitted to PR
Interface Simulation Distances
The classical (boolean) notion of refinement for behavioral interfaces of
system components is the alternating refinement preorder. In this paper, we
define a distance for interfaces, called interface simulation distance. It
makes the alternating refinement preorder quantitative by, intuitively,
tolerating errors (while counting them) in the alternating simulation game. We
show that the interface simulation distance satisfies the triangle inequality,
that the distance between two interfaces does not increase under parallel
composition with a third interface, and that the distance between two
interfaces can be bounded from above and below by distances between
abstractions of the two interfaces. We illustrate the framework, and the
properties of the distances under composition of interfaces, with two case
studies.Comment: In Proceedings GandALF 2012, arXiv:1210.202
On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly
In the field of face recognition, Sparse Representation (SR) has received
considerable attention during the past few years. Most of the relevant
literature focuses on holistic descriptors in closed-set identification
applications. The underlying assumption in SR-based methods is that each class
in the gallery has sufficient samples and the query lies on the subspace
spanned by the gallery of the same class. Unfortunately, such assumption is
easily violated in the more challenging face verification scenario, where an
algorithm is required to determine if two faces (where one or both have not
been seen before) belong to the same person. In this paper, we first discuss
why previous attempts with SR might not be applicable to verification problems.
We then propose an alternative approach to face verification via SR.
Specifically, we propose to use explicit SR encoding on local image patches
rather than the entire face. The obtained sparse signals are pooled via
averaging to form multiple region descriptors, which are then concatenated to
form an overall face descriptor. Due to the deliberate loss spatial relations
within each region (caused by averaging), the resulting descriptor is robust to
misalignment & various image deformations. Within the proposed framework, we
evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder
Neural Network (SANN), and an implicit probabilistic technique based on
Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and
ChokePoint datasets show that the proposed local SR approach obtains
considerably better and more robust performance than several previous
state-of-the-art holistic SR methods, in both verification and closed-set
identification problems. The experiments also show that l1-minimisation based
encoding has a considerably higher computational than the other techniques, but
leads to higher recognition rates
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