2,624 research outputs found
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
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
Forward transition rates
The idea of forward rates stems from interest rate theory. It has natural
connotations to transition rates in multi-state models. The generalization from
the forward mortality rate in a survival model to multi-state models is
non-trivial and several definitions have been proposed. We establish a
theoretical framework for the discussion of forward rates. Furthermore, we
provide a novel definition with its own logic and merits and compare it with
the proposals in the literature. The definition turns the Kolmogorov forward
equations inside out by interchanging the transition probabilities with the
transition intensities as the object to be calculated.Comment: Revision of manuscript. The manuscript now contains a section on
'Forward-thinking and actuarial practice'. Furthermore, we have corrected
typos and re-written certain sentences to improve readability and accurac
Multi-Factor Key Derivation Function (MFKDF) for Fast, Flexible, Secure, & Practical Key Management
We present the first general construction of a Multi-Factor Key Derivation
Function (MFKDF). Our function expands upon password-based key derivation
functions (PBKDFs) with support for using other popular authentication factors
like TOTP, HOTP, and hardware tokens in the key derivation process. In doing
so, it provides an exponential security improvement over PBKDFs with less than
12 ms of additional computational overhead in a typical web browser. We further
present a threshold MFKDF construction, allowing for client-side key recovery
and reconstitution if a factor is lost. Finally, by "stacking" derived keys, we
provide a means of cryptographically enforcing arbitrarily specific key
derivation policies. The result is a paradigm shift toward direct cryptographic
protection of user data using all available authentication factors, with no
noticeable change to the user experience. We demonstrate the ability of our
solution to not only significantly improve the security of existing systems
implementing PBKDFs, but also to enable new applications where PBKDFs would not
be considered a feasible approach.Comment: To appear in USENIX Security '2
Privacy-preserving architecture for forensic image recognition
Forensic image recognition is an important tool in many areas of law enforcement where an agency wants to prosecute possessors of illegal images. The recognition of illegal images that might have undergone human imperceptible changes (e.g., a JPEG-recompression) is commonly done by computing a perceptual image hash function of a given image and then matching this hash with perceptual hash values in a database of previously collected illegal images. To prevent privacy violation, agencies should only learn about images that have been reliably detected as illegal and nothing else. In this work, we argue that the prevalent presence of separate departments in such agencies can be used to enforce the need-to-know principle by separating duties among them. This enables us to construct the first practically efficient architecture to perform forensic image recognition in a privacy-preserving manner. By deriving unique cryptographic keys directly from the images, we can encrypt all sensitive data and ensure that only illegal images can be recovered by the law enforcement agency while all other information remains protected
Polar Coding for Secret-Key Generation
Practical implementations of secret-key generation are often based on
sequential strategies, which handle reliability and secrecy in two successive
steps, called reconciliation and privacy amplification. In this paper, we
propose an alternative approach based on polar codes that jointly deals with
reliability and secrecy. Specifically, we propose secret-key capacity-achieving
polar coding schemes for the following models: (i) the degraded binary
memoryless source (DBMS) model with rate-unlimited public communication, (ii)
the DBMS model with one-way rate-limited public communication, (iii) the 1-to-m
broadcast model and (iv) the Markov tree model with uniform marginals. For
models (i) and (ii) our coding schemes remain valid for non-degraded sources,
although they may not achieve the secret-key capacity. For models (i), (ii) and
(iii), our schemes rely on pre-shared secret seed of negligible rate; however,
we provide special cases of these models for which no seed is required.
Finally, we show an application of our results to secrecy and privacy for
biometric systems. We thus provide the first examples of low-complexity
secret-key capacity-achieving schemes that are able to handle vector
quantization for model (ii), or multiterminal communication for models (iii)
and (iv).Comment: 26 pages, 9 figures, accepted to IEEE Transactions on Information
Theory; parts of the results were presented at the 2013 IEEE Information
Theory Worksho
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