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S. Gnana Sophia, K.K Thanammal And S SSujatha
2021 Zenodo  
In this paper wereviewe the homomorphic encryption techniques, Advantages and disadvantages of various research papers.  ...  The information will be encrypted and gathered in the storage of cloud .The problem here is the informations can be send to or from a cloud in encrypted mode, the servers that function the cloud will not  ...  They add a novel kernel HE schemes to reduce the development of noises and computation. By using kernel and kernel homomorphism, the Ker-HE scheme was used to remove the number of noises.  ... 
doi:10.5281/zenodo.5166891 fatcat:olzgoycipfdwnce4iwl6vfkopu

Security-preserving Support Vector Machine with Fully Homomorphic Encryption

Saerom Park, Jaewook Lee, Jung Hee Cheon, Juhee Lee, Jaeyun Kim, Junyoung Byun
2019 AAAI Conference on Artificial Intelligence  
The proposed procedure includes our protocol, data structure and homomorphic evaluation.  ...  In this paper, we want to propose a security-preserving learning framework using fully homomorphic encryption for support vector machine model.  ...  Design Components Fully Homomorphic Encryption Fully homomorphic encryption (FHE) is a cryptographic scheme which aims to enable homomorphic operations such Copyright held by authors as additions and multiplications  ... 
dblp:conf/aaai/Park0CLKB19 fatcat:qdk4pwus25gmhmxebygcb3cagy

Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

Abeer D. Algarni, Ghada M. El Banby, Naglaa F. Soliman, Fathi E. Abd El-Samie, Abdullah M. Iliyasu
2020 Electronics  
In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits.  ...  Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits.  ...  Another effective metric widely used in assessing the efficiency of encryption protocols is the correlation coefficient, which, in our case, measures the correlation between the encrypted images stored  ... 
doi:10.3390/electronics9061046 fatcat:xrdumgtdg5cqfiaamhjvzcqj74

Impala: Low-Latency, Communication-Efficient Private Deep Learning Inference [article]

Woo-Seok Choi, Brandon Reagen, Gu-Yeon Wei, David Brooks
2022 arXiv   pre-print
Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC).  ...  This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting.  ...  The computational complexity of homomorphic convolution with the kernel parameters (c i , c o , f w , w) is O(c i c o f 2 w ).  ... 
arXiv:2205.06437v1 fatcat:4gh422lyafe33kiqfipcgwwmyu

Efficient Biometric Verification in Encrypted Domain [chapter]

Maneesh Upmanyu, Anoop M. Namboodiri, K. Srinathan, C. V. Jawahar
2009 Lecture Notes in Computer Science  
The protocol uses asymmetric encryption, and captures the advantages of biometric authentication.  ...  In this paper, we propose an authentication protocol that alleviates these concerns. The protocol takes care of user privacy, template protection and trust issues in biometric authentication systems.  ...  To achieve this, we use a class of encryptions that are multiplicative homomorphic [14] .  ... 
doi:10.1007/978-3-642-01793-3_91 fatcat:4cfkanbkcrek5gnx27hzla4e6m

Notes on non-interactive secure comparison in "image feature extraction in the encrypted domain with privacy-preserving SIFT"

Matthias Schneider, Thomas Schneider
2014 Proceedings of the 2nd ACM workshop on Information hiding and multimedia security - IH&MMSec '14  
As alternatives we propose to use either interactive comparison protocols or non-interactive somewhat or fully homomorphic encryption.  ...  Their fundamental building block is a new protocol for performing secure comparisons under additively homomorphic encryption that requires no interaction.  ...  Additively Homomorphic Encryption The protocols of [HLP12] are based on the additively homomorphic cryptosystem of Paillier [Pai99] as described next.  ... 
doi:10.1145/2600918.2600927 dblp:conf/ih/SchneiderS14 fatcat:hifivsismre4law5jn6hpmmrp4

Cryptographically private support vector machines

Sven Laur, Helger Lipmaa, Taneli Mielikäinen
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
The new protocols return their outputs-either the kernel value, the classifier or the classificationsin encrypted form so that they can be decrypted only by a common agreement by the protocol participants  ...  More specifically, we propose private protocols implementing the Kernel Adatron and Kernel Perceptron learning algorithms, give private classification protocols and private polynomial kernel computation  ...  Acknowledgments We wish to thank Matti Kääriäinen, Juho Rousu and Sandor Szedmak for valuable discussions on the nature of SVMs and the current state of the art in kernel methods.  ... 
doi:10.1145/1150402.1150477 dblp:conf/kdd/LaurLM06 fatcat:bb5pdymn4ff7leqx2hm3unz2f4

Privacy-Preserving Clinical Decision Support System Using Gaussian Kernel-Based Classification

Yogachandran Rahulamathavan, Suresh Veluru, Raphael C.-W Phan, Jonathon A. Chambers, Muttukrishnan Rajarajan
2014 IEEE journal of biomedical and health informatics  
Privacy-preserving clinical decision support system using gaussian kernel-based classication.  ...  Hence, to preserve privacy, they developed a protocol to perform secure kernel sharing, prediction and training using secret sharing and homomorphic encryption techniques.  ...  Homomorphic Encryption One of the building blocks for our technique is homomorphic encryption.  ... 
doi:10.1109/jbhi.2013.2274899 pmid:24403404 fatcat:rvtb47m4fvfrpoafusz6jg2fgu

Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud

Yogachandran Rahulamathavan, Raphael C.-W. Phan, Suresh Veluru, Kanapathippillai Cumanan, Muttukrishnan Rajarajan
2014 IEEE Transactions on Dependable and Secure Computing  
The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation.  ...  At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers.  ...  Hence, in order to preserve the privacy they developed a protocol to perform secure kernel sharing, prediction and training using secret sharing and homomorphic encryption techniques.  ... 
doi:10.1109/tdsc.2013.51 fatcat:zsiuletsb5gqpbtjrqavdk6ucy

MUSKETEER D4.1 Investigative overview of targeted architecture and algorithms

Roberto Diaz, Ángel Navia, Francisco J. González
2019 Zenodo  
We could use any of the available protocols, for instance: • protocols relying on homomorphic encryption, such as [Zhang_2017] • protocols relying on SMC, such as [Zhu_2015] • secure matrix multiplication  ...  [Armknecht_2015] Some common types of homomorphic encryption are partially homomorphic, somewhat homomorphic, levelled fully homomorphic, and fully homomorphic encryption.  ... 
doi:10.5281/zenodo.4736796 fatcat:tdbukkedfjd2rnisify2ebd3u4

Privacy-Preserving Fair Learning of Support Vector Machine with Homomorphic Encryption

Saerom Park, Junyoung Byun, Joohee Lee
2022 Proceedings of the ACM Web Conference 2022  
In this study, we propose a privacy-preserving training algorithm for a fair support vector machine classifier based on Homomorphic Encryption (HE), where the privacy of both sensitive information and  ...  Algorithm 1 describes our Fair SVM training with Homomorphic Encryption (FSVMHE).  ...  Homomorphic Encryption (HE) is a promising primitive for secure computations, which supports arithmetic computations on encrypted data without decryption, allowing data to be utilized without compromising  ... 
doi:10.1145/3485447.3512252 fatcat:zwmwqjl42zgm7a3c57x3ewjn7y

Privacy-Preserving Outsourcing Scheme for SVM on Vertically Partitioned Data

Guowei Qiu, Hua Huo, Xiaolin Gui, Huijun Dai
2022 Security and Communication Networks  
In this paper, by using additive homomorphic encryption and random transformations (matrix transformation and vector decomposition), we design a privacy-preserving outsourcing scheme for conducting Least  ...  In our system, multiple data owners (users) submit their encrypted data to two non-colluding service providers, which conduct SVM algorithm on it.  ...  It performs all homomorphic computations in our protocols.  ... 
doi:10.1155/2022/9983463 doaj:37c711b2f0e7412f8b3613f3a482db11 fatcat:mv475eg6zrbtzikg52rtczk2sq

HE-friendly algorithm for privacy-preserving SVM training

Saerom Park, Junyoung Byun, Joohee Lee, Jung-Hee Cheon, Jaewook Lee
2020 IEEE Access  
The inference phase is also implemented on the encrypted domain with fully-homomorphic encryption which enables real-time prediction.  ...  INDEX TERMS Cryptography, data privacy, fully homomorphic encryption, support vector machine, privacy-preserving training.  ...  HOMOMORPHIC ENCRYPTION FHE is a cryptosystem which enables homomorphic operations such as additions and multiplications on encrypted data.  ... 
doi:10.1109/access.2020.2981818 fatcat:6rrd4imtofaspoyy2fhvrkgz44

Gazelle: A Low Latency Framework for Secure Neural Network Inference [article]

Chiraag Juvekar, Vinod Vaikuntanathan, Anantha Chandrakasan
2018 arXiv   pre-print
Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference.  ...  Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines.  ...  FAST HOMOMORPHIC CONVOLUTIONS We now move on the implementation of homomorphic kernels for Conv layers.  ... 
arXiv:1801.05507v1 fatcat:sbo4nsu3ufbahfmfmeegqter2a

MAS-Encryption and Its Applications in Privacy-Preserving Classifiers

Chongzhi Gao, Jin Li, Shibing Xia, Kim-Kwang Raymond Choo, Wenjing Lou, Changyu Dong
2020 IEEE Transactions on Knowledge and Data Engineering  
Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information  ...  The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two  ...  PHE mainly includes additively homomorphic encryption (AHE) and multiplicatively homomorphic encryption (MHE).  ... 
doi:10.1109/tkde.2020.3009221 fatcat:6g7f3r5pprelfb3355xgf6ulo4
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