The Experts below are selected from a list of 10611 Experts worldwide ranked by ideXlab platform

Vinod Vaikuntanathan - One of the best experts on this subject based on the ideXlab platform.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies
    BMC Medical Genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Background Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH’18 Track 2: “Secure Parallel Genome Wide Association Studies using HE”. Conclusions Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies.
    BMC medical genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

Wassim Hamidouche - One of the best experts on this subject based on the ideXlab platform.

  • Privacy Protection in Real Time HEVC Standard Using Chaotic System
    Cryptography, 2020
    Co-Authors: Mohammed Abu Taha, Naty Sidaty, Wassim Hamidouche, Safwan El Assad, Marko Viitanen, Jarno Vanne, Olivier Deforges
    Abstract:

    Video protection and access control have gathered steam over recent years. However, the most common methods encrypt the whole video bit stream as unique data without taking into account the structure of the compressed video. These full Encryption Solutions are time and power consuming and, thus, are not aligned with the real-time applications. In this paper, we propose a Selective Encryption (SE) Solution for Region of Interest (ROI) security based on the tile concept in High Efficiency Video Coding (HEVC) standards and selective Encryption of all sensitive parts in videos. The SE Solution depends on a chaos-based stream cipher that encrypts a set of HEVC syntax elements normatively, that is, the bit stream can be decoded with a standard HEVC decoder, and a secret key is only required for ROI decryption. The proposed ROI Encryption Solution relies on the independent tile concept in HEVC that splits the video frame into independent rectangular areas. Tiles are used to pull out the ROI from the background and only the tiles figuring the ROI are encrypted. In inter coding, the independence of tiles is guaranteed by limiting the motion vectors of non-ROI to use only the unencrypted tiles in the reference frames. Experimental results have shown that the Encryption Solution performs secure video Encryption in a real time context, with a diminutive bit rate and complexity overheads.

  • Real-Time Selective Encryption Solution based on ROI for MPEG-A Visual Identity Management AF
    2017
    Co-Authors: Cyril Bergeron, Naty Sidaty, Wassim Hamidouche, Benoit Boyadjis, Jean Le Feuvre, Lim Youngkwon
    Abstract:

    As part of a new MPEG-A standardization activity, called Visual Identity Management Application Format (VIMAF), this paper presents an end-to-end Encryption Solution of Region of Interest (ROI) in both AVC and HEVC encoded streams for privacy protection applications. This Solution uses a selective Encryption method that encrypts only the most sensitive information of the video and proposes a new adapted syntax in order to facilitate interoperability between equipments. Objective video quality measurements have shown the robustness of the proposed selective Encryption Solution with only a slight bitrate increase.

  • DSP - Real-time selective Encryption Solution based on ROI for MPEG-A visual identity management AF
    2017 22nd International Conference on Digital Signal Processing (DSP), 2017
    Co-Authors: Cyril Bergeron, Naty Sidaty, Wassim Hamidouche, Benoit Boyadjis, Jean Le Feuvre, Young-kwon Lim
    Abstract:

    As part of a new MPEG-A standardization activity, called Visual Identity Management Application Format (VIMAF), this paper presents an end-to-end Encryption Solution of Region of Interest (ROI) in both AVC and HEVC encoded streams for privacy protection applications. This Solution uses a selective Encryption method that encrypts only the most sensitive information of the video and proposes a new adapted syntax in order to facilitate interoperability between equipment. Objective video quality measurements have shown the robustness of the proposed selective Encryption Solution with only a slight bitrate increase.

  • Real-Time Selective Video Encryption based on the Chaos System in Scalable HEVC Extension
    Signal Processing: Image Communication, 2017
    Co-Authors: Wassim Hamidouche, Naty Sidaty, Mousa Farajallah, Safwan El Assad, Olivier Deforges
    Abstract:

    In this paper we propose a real-time selective video Encryption Solution in the scalable extension of High Effciency Video Coding (HEVC) standard, referred to as SHVC. The proposed scheme encrypts a set of sensitive SHVC parameters with a minimum delay and complexity overheads. The Encryption process is performed at the CABAC binstring level and fulfils both constant bitrate and format compliant video Encryption requirements. In addition, it preserves all SHVC functionalities, including bitstream extraction for mid-network adaptation and error resilience. We compare the performance of three selective SHVC Encryption schemes: the first scheme encrypts only the lowest SHVC layer, the second encrypts all layers and the last scheme encrypts only the highest layer. The performance of the proposed schemes is assessed over different video Encryption criteria, at different scalability configurations and various High Definition (HD) video sequences. Experimental results showed that encrypt only the lowest layer or all layers enables a high security level, while encrypting only the highest layer leads to a perceptual Encryption Solution, by slightly decreasing the highest layer quality. Moreover, the processing complexity of the proposed Solution is assessed in the context of a real-time SHVC decoder. The complexity overhead remains low and does not exceed 6% of the real-time decoding of SHVC video sequences.

  • Selective video Encryption using chaotic system in the SHVC extension
    2015
    Co-Authors: Wassim Hamidouche, Mousa Farajallah, Olivier Deforges, Mickael Raulet, Safwan El Assad
    Abstract:

    In this paper we investigate a selective video Encryption in the scalable HEVC extension (SHVC). The SHVC extension encodes the video in several layers corresponding to different spatial and quality representations of the video. We propose a selective Encryption Solution using a chaotic-based Encryption system. The proposed Solution encrypts a set of sensitive parameters with a minimum complexity overhead, at constant bitrate and SHVC format compliant. Experimental results compare the performance of three Encryption schemes: encrypt only the lowest layer, all layers, and only the highest layer. The first two schemes achieve a high security level with a drastic degradation in the decoded video, while the last scheme enables a perceptual video Encryption by decreasing the quality of the highest layer below the quality of the clear layers.

Marcelo Blatt - One of the best experts on this subject based on the ideXlab platform.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies
    BMC Medical Genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Background Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH’18 Track 2: “Secure Parallel Genome Wide Association Studies using HE”. Conclusions Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies.
    BMC medical genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

Alexander Gusev - One of the best experts on this subject based on the ideXlab platform.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies
    BMC Medical Genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Background Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH’18 Track 2: “Secure Parallel Genome Wide Association Studies using HE”. Conclusions Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies.
    BMC medical genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

Kurt Rohloff - One of the best experts on this subject based on the ideXlab platform.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies
    BMC Medical Genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Background Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH’18 Track 2: “Secure Parallel Genome Wide Association Studies using HE”. Conclusions Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.

  • Optimized homomorphic Encryption Solution for secure genome-wide association studies.
    BMC medical genomics, 2020
    Co-Authors: Marcelo Blatt, Alexander Gusev, Yuriy Polyakov, Kurt Rohloff, Vinod Vaikuntanathan
    Abstract:

    Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. We present a Solution for secure GWAS using homomorphic Encryption (HE) that keeps all individual data encrypted throughout the association study. Our Solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our Solution for a smaller dataset was awarded co-first place in iDASH'18 Track 2: "Secure Parallel Genome Wide Association Studies using HE". Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.