References of "Demirci, Huseyin 50039300"
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See detailA Secure Authentication Protocol for Cholesteric Spherical Reflectors using Homomorphic Encryption
Arenas Correa, Monica Patricia UL; Bingol, Muhammed Ali; Demirci, Huseyin UL et al

in Lecture Notes in Computer Science (2022, July)

Sometimes fingerprint-like features are found in a material. The exciting discovery poses new challenges on how to use the features to build an object authentication protocol that could tell customers and ... [more ▼]

Sometimes fingerprint-like features are found in a material. The exciting discovery poses new challenges on how to use the features to build an object authentication protocol that could tell customers and retailers equipped with a mobile device whether a good is authentic or fake. We are exactly in this situation with Cholesteric Spherical Reflectors (CSRs), tiny spheres of liquid crystals with which we can tag or coat objects. They are being proposed as a potential game-changer material in anti-counterfeiting due to their unique optical properties. In addition to the problem of processing images and extracting the minutiæ embedded in a CSR, one major challenge is designing cryptographically secure authentication protocols. The authentication procedure has to handle unstable input data; it has to measure the distance between some reference data stored at enrollment and noisy input provided at authentication. We propose a cryptographic authentication protocol that solves the problem, and that is secure against semi-honest and malicious adversaries. We prove that our design ensures data privacy even if enrolled data are leaked and even if servers and provers are actively curious. We implement and benchmark the protocol in Python using the Microsoft SEAL library through its Python wrapper PySEAL. [less ▲]

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See detailAn Analysis of Cholesteric Spherical Reflector Identifiers for Object Authenticity Verification
Arenas Correa, Monica Patricia UL; Demirci, Huseyin UL; Lenzini, Gabriele UL

in Machine Learning and Knowledge Extraction (2022), 4(1), 222-239

Arrays of Cholesteric Spherical Reflectors (CSRs), microscopic cholesteric liquid crystals in a spherical shape, have been argued to become a game-changing technology in anti-counterfeiting. Used to build ... [more ▼]

Arrays of Cholesteric Spherical Reflectors (CSRs), microscopic cholesteric liquid crystals in a spherical shape, have been argued to become a game-changing technology in anti-counterfeiting. Used to build identifiable tags or coating, called CSR IDs, they can supply objects with unclonable fingerprint-like characteristics, making it possible to authenticate objects. In a previous study, we have shown how to extract minutiæ from CSR IDs. In this journal version, we build on that previous research, consolidate the methodology, and test it over CSR IDs obtained by different production processes. We measure the robustness and reliability of our procedure on large and variegate sets of CSR IDs’ images taken with a professional microscope (Laboratory Data set) and with a microscope that could be used in a realistic scenario (Realistic Data set). We measure intra-distance and interdistance, proving that we can distinguish images coming from the same CSR ID from images of different CSR IDs. However, without surprise, images in Laboratory Data set have an intra-distance that on average is less, and with less variance, than the intra-distance between responses from Realistic Data set. With this evidence, we discuss a few requirements for an anti-counterfeiting technology based on CSRs. [less ▲]

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See detailPrivacy-preserving Copy Number Variation Analysis with Homomorphic Encryption
Demirci, Huseyin UL; Lenzini, Gabriele UL

Scientific Conference (2022)

Innovative pharma-genomics and personalized medicine services are now possible thanks to the availability for processing and analysis of a large amount of genomic data. Operating on such databases, is ... [more ▼]

Innovative pharma-genomics and personalized medicine services are now possible thanks to the availability for processing and analysis of a large amount of genomic data. Operating on such databases, is possible to test for predisposition to diseases by searching for genomic variants on whole genomes as well as on exomes, which are collections of protein coding regions called exons. Genomic data are therefore shared amongst research institutes, public/private operators, and third parties, creating issues of privacy, ethics, and data protection because genome data are strictly personal and identifying. To prevent damages that could follow a data breach—a likely threat nowadays—and to be compliant with current data protection regulations, genomic data files should be encrypted, and the data processing algorithms should be privacy-preserving. Such a migration is not always feasible: not all operations can be implemented straightforwardly to be privacypreserving; a privacy-preserving version of an algorithm may not be as accurate for the purpose of biomedical analysis as the original; or the privacy-preserving version may not scale up when applied to genomic data processing because of inefficiency in computation time. In this work, we demonstrate that at least for a wellknown genomic data procedure for the analysis of copy number variants called copy number variations (CNV) a privacy-preserving analysis is possible and feasible. Our algorithm relies on Homomorphic Encryption, a cryptographic technique to perform calculations directly on the encrypted data. We test our implementation for performance and reliability, giving evidence that it is practical to study copy number variations and preserve genomic data privacy. Our proof-of-concept application successfully and efficiently searches for a patient’s somatic copy number variation changes by comparing the patient gene coverage in the whole exome with a healthy control exome coverage. Since all the genomics data are securely encrypted, the data remain protected even if they are transmitted or shared via an insecure environment like a public cloud. Being this the first study for privacy-preserving copy number variation analysis, we demonstrate the potential of recent Homomorphic Encryption tools in genomic applications. [less ▲]

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See detailCholesteric Spherical Reflectors as Physical Unclonable Identifiers in Anti-counterfeiting
Arenas Correa, Monica Patricia UL; Demirci, Huseyin UL; Lenzini, Gabriele UL

in Journal of the Association for Computing Machinery (2021, August 17), 16

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