Secure and verifiable iris authentication system using fully homomorphic encryption

Morampudi, Mahesh Kumar; Prasad, Munaga V. N. K.; Verma, Mridula; Raju, U. S. N.

doi:10.1016/j.compeleceng.2020.106924

Cited by 25 publications

(17 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These exciting capabilities have attracted the interest of researchers from various fields who have tried to understand the applicability of HE on their different use cases. Recently, Morumpudi et al [9] use HE in iris authentication systems. Similarly, Reagen et al [10] and Wood et al [11] comprehensively analyze the applicability of HE in the field of healthcare.…”

Section: Related Workmentioning

confidence: 99%

“…A systematic review is performed by Yousuf et al [12], where they provide a broad overview of how HE is being used in various domains and what the underlying technology is about. The merger of federated learning and HE is also an exciting feature and some researchers have tried to explore this [9], [13]. BatchCrypt [14] implements HE as a pluggable module on the client side, in a federated learning system.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

Argani¹,

Javed²,

Qayyum³

et al. 2022

Preprint

View full text Add to dashboard Cite

In addition to threatening human lives, the recent COVID-19 pandemic has also highlighted how misinformation is plaguing our online social networks. However, privacy and ethical concerns reduce data sharing by stakeholders, impeding data-driven misinformation detection. Current data encryption techniques---providing privacy guarantees on data---cannot be naively extended to inferring text inputs with Deep Learning (DL) models mainly due to inherent non-polynomial operations (which are not encryption-compatible), error introduced by approximate polynomial activations (because they are only valid for a limited range of input values), and error accumulated over stacked encrypted operations of DL classifiers. In this paper, we propose encrypted federated learning (EFL) framework for text-based misinformation detection as a (secure and privacy-aware cloud) service, where classifiers are securely trained in FL framework---which ensures the privacy of training data holders---and later inference is performed on homomorphically encrypted data---which ensures the privacy of clients' data. We evaluate three classifier architectures---Logistic Regression (LR), Multilayer Perceptron (MLP), and the novel encryption-compatible self-attention network (SAN) proposed in this paper---on two publicly available text-based misinformation detection datasets. To reduce the error induced by polynomial activations, we, formally and empirically, show the efficacy of L<sub>2</sub> regularization while training the classifier. To regulate the error accumulated due to cascaded operations over encrypted data, we advocate the use of sigmoid activation with formal proofs and empirically validate our claims. In our case, by simply replacing ReLU activation with sigmoid, we were able to reduce the output error by 1750 times in the best case to 43.75 times in the worst case.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

Argani¹,

Javed²,

Qayyum³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These exciting capabilities have attracted the interest of researchers from various fields who have tried to understand the applicability of HE on their different use cases. Recently, Morumpudi et al [22] use HE in iris authentication systems. Similarly, Reagen et al [23] and Wood et al [24] comprehensively analyze the applicability of HE in the field of healthcare.…”

Section: Related Workmentioning

confidence: 99%

“…A systematic review is performed by Yousuf et al [25], where they provide a broad overview of how HE is being used in various domains and what the underlying technology is about. The merger of federated learning and HE is also an exciting feature and some researchers have tried to explore this [22], [26]. BatchCrypt [27] implements HE as a pluggable module on the client side, in a federated learning system.…”

Section: Related Workmentioning

confidence: 99%

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

Argani¹,

Javed²,

Qayyum³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Recent works have highlighted how misinformation is plaguing our online social networks. Numerous algorithms on automated misinformation detection are centered around deep learning~(DL) which requires large data for training. However, privacy and ethical concerns reduce data sharing by stakeholders, impeding data-driven misinformation detection. Current data encryption techniques providing privacy guarantees cannot be naively extended to text inference with DL models, mainly due to the errors induced by stacked encrypted operations and polynomial approximations of the otherwise encryption-incompatible non-polynomial operations. In this paper, we show, formally and empirically, the effectiveness of (1) $L_2$ regularized training to reduce the overall error induced by approximate polynomial activations, and (2) sigmoid activation to regulate the error accumulated due to cascaded operations over encrypted data. We assume a federated learning-encrypted inference~(FL-EI) setup for text-based misinformation detection as a (secure and privacy-aware cloud) service, where classifiers are securely trained in FL framework and inference is performed on homomorphically encrypted data. We evaluate three architectures---Logistic Regression~(LR), Multilayer Perceptron~(MLP), and Self-Attention Network~(SAN)---on two public text-misinformation datasets with some interesting results, for example, by simply replacing ReLU activation with sigmoid, we were able to reduce the output error by $1750\times$ in the best case to $43.75\times$ in the worst case.</p>

show abstract

“…Homomorphic encryption is utilized in a few studies where the biometric data were outsourced to the server. The following studies utilized homomorphic encryption for privacy-preserving biometric authentication: [ 149 , 150 , 151 , 152 , 153 , 154 , 155 ].…”

Section: Recommendations For Future Research Directionsmentioning

confidence: 99%

Security, Privacy, and Usability in Continuous Authentication: A Survey

Baig

Eskeland

2021

Sensors

View full text Add to dashboard Cite

Continuous authentication has been proposed as a possible approach for passive and seamless user authentication, using sensor data comprising biometric, behavioral, and context-oriented characteristics. Since these are personal data being transmitted and are outside the control of the user, this approach causes privacy issues. Continuous authentication has security challenges concerning poor matching rates and susceptibility of replay attacks. The security issues are mainly poor matching rates and the problems of replay attacks. In this survey, we present an overview of continuous authentication and comprehensively discusses its different modes, and issues that these modes have related to security, privacy, and usability. A comparison of privacy-preserving approaches dealing with the privacy issues is provided, and lastly recommendations for secure, privacy-preserving, and user-friendly continuous authentication.

show abstract

Secure and verifiable iris authentication system using fully homomorphic encryption

Cited by 25 publications

References 18 publications

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

SPAM-DaS: Secure and Privacy-Aware Textual Misinformation Detection as a Service

Security, Privacy, and Usability in Continuous Authentication: A Survey

Contact Info

Product

Resources

About