1,049 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
Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography
Several recent works have proposed and implemented cryptography as a means to
preserve privacy and security of patients health data. Nevertheless, the
weakest point of electronic health record (EHR) systems that relied on these
cryptographic schemes is key management. Thus, this paper presents the
development of privacy and security system for cryptography-based-EHR by taking
advantage of the uniqueness of fingerprint and iris characteristic features to
secure cryptographic keys in a bio-cryptography framework. The results of the
system evaluation showed significant improvements in terms of time efficiency
of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
likelihood of imposters gaining successful access to the keys protecting
patients protected health information. This result also justifies the
feasibility of implementing fuzzy key binding scheme in real applications,
especially fuzzy vault which demonstrated a better performance during key
reconstruction
Multi-dimensional key generation of ICMetrics for cloud computing
Despite the rapid expansion and uptake of cloud based services, lack of trust in the provenance of such services represents a significant inhibiting factor in the further expansion of such service. This paper explores an approach to assure trust and provenance in cloud based services via the generation of digital signatures using properties or features derived from their own construction and software behaviour. The resulting system removes the need for a server to store a private key in a typical Public/Private-Key Infrastructure for data sources. Rather, keys are generated at run-time by features obtained as service execution proceeds. In this paper we investigate several potential software features for suitability during the employment of a cloud service identification system. The generation of stable and unique digital identity from features in Cloud computing is challenging because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Subsequently, a smooth entropy algorithm is developed to evaluate the entropy of key space
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
We provide formal definitions and efficient secure techniques for
- turning noisy information into keys usable for any cryptographic
application, and, in particular,
- reliably and securely authenticating biometric data.
Our techniques apply not just to biometric information, but to any keying
material that, unlike traditional cryptographic keys, is (1) not reproducible
precisely and (2) not distributed uniformly. We propose two primitives: a
"fuzzy extractor" reliably extracts nearly uniform randomness R from its input;
the extraction is error-tolerant in the sense that R will be the same even if
the input changes, as long as it remains reasonably close to the original.
Thus, R can be used as a key in a cryptographic application. A "secure sketch"
produces public information about its input w that does not reveal w, and yet
allows exact recovery of w given another value that is close to w. Thus, it can
be used to reliably reproduce error-prone biometric inputs without incurring
the security risk inherent in storing them.
We define the primitives to be both formally secure and versatile,
generalizing much prior work. In addition, we provide nearly optimal
constructions of both primitives for various measures of ``closeness'' of input
data, such as Hamming distance, edit distance, and set difference.Comment: 47 pp., 3 figures. Prelim. version in Eurocrypt 2004, Springer LNCS
3027, pp. 523-540. Differences from version 3: minor edits for grammar,
clarity, and typo
A novel cryptosystem based on iris key generation
Author name used in this publication: David ZhangBiometrics Research Centre, Department of ComputingVersion of RecordPublishe
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