Showing papers on "Homomorphic encryption published in 2021"

Circuit Copyright Blockchain: Blockchain-Based Homomorphic Encryption for IP Circuit Protection

Abstract: The fast development of Blockchain technology makes it widely applied in several fields of digital transactions, like e-government affairs and the protection of financial transactions. In this article, we propose a homomorphic encryption-based Blockchain for circuit copyright protection that effectively addresses the issues in the protection of circuit copyright transactions, such as low security of private data, low efficiency in transaction data storage, cooperation and supervision. First, we establish a homomorphic encryption-based mathematical model by utilizing Blockchain and intelligent contract, and next, the algorithms that include Blockchain generation, homomorphic chain encryption/decryption, and intelligent contract are designed. As the intelligent contract is correctly executed in Blockchain, a fully homomorphic encryption-based identity authentication protocol is tackled for Blockchain, given that it ensures the change operation of any third-party in Blockchain and realizes real-time verification. The system is apposite for circuit copyright protection in a blockchain network, due to the use of distributed identity authentication and real-time extensible storage improves the security and extensibility of blockchain-based circuit copyright protection. The experimental results show that the proposed algorithm has reduced the transmission cost and improved the efficiency of data storage and supervision. In addition, it is resilient to several common attacks (e.g., double-spending attacks), yet incurs low cost/overhead and has a higher level of security when compared to three other competing algorithms.

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Abstract: Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption

Abstract: Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE’s widespread adoption. We present F1, the first FHE accelerator that is programmable, i.e., capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the key bottleneck. Thus, F1 is primarily designed to minimize data movement. Hardware provides an explicitly managed memory hierarchy and mechanisms to decouple data movement from execution. A novel compiler leverages these mechanisms to maximize reuse and schedule off-chip and on-chip data movement. We evaluate F1 using cycle-accurate simulation and RTL synthesis. F1 is the first system to accelerate complete FHE programs, and outperforms state-of-the-art software implementations by gmean 5,400 × and by up to 17,000 ×. These speedups counter most of FHE’s overheads and enable new applications, like real-time private deep learning in the cloud.

Blockchain-enabled Federated Learning Data Protection Aggregation Scheme with Differential Privacy and Homomorphic Encryption in IIoT

Abstract: With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, Federated Learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g. model extraction attack and model reverse attack) in Federated Learning. To address the above problems, we first design an application model of blockchain-enabled Federated Learning in IIoT, and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed Random Forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and Federated Learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data With GPUs

Abstract: Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is the user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this article, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved sufficient security level ( $> 80$ > 80 bit) and reasonable classification accuracy (99) and (77.55 percent) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images ( $>$ > 8,000) without extra overhead.

On the Security of Homomorphic Encryption on Approximate Numbers

Abstract: We present passive attacks against CKKS, the homomorphic encryption scheme for arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both theoretically efficient (running in expected polynomial time) and very practical, leading to complete key recovery with high probability and very modest running times. We implemented and tested the attack against major open source homomorphic encryption libraries, including HEAAN, SEAL, HElib and PALISADE, and when computing several functions that often arise in applications of the CKKS scheme to machine learning on encrypted data, like mean and variance computations, and approximation of logistic and exponential functions using their Maclaurin series.

Flexible and Efficient Verifiable Computation on Encrypted Data

Abstract: We consider the problem of verifiable and private delegation of computation [Gennaro et al. CRYPTO’10] in which a client stores private data on an untrusted server and asks the server to compute functions over this data. In this scenario we aim to achieve three main properties: the server should not learn information on inputs and outputs of the computation (privacy), the server cannot return wrong results without being caught (integrity), and the client can verify the correctness of the outputs faster than running the computation (efficiency). A known paradigm to solve this problem is to use a (non-private) verifiable computation (VC) to prove correctness of a homomorphic encryption (HE) evaluation on the ciphertexts. Despite the research advances in obtaining efficient VC and HE, using these two primitives together in this paradigm is concretely expensive. Recent work [Fiore et al. CCS’14, PKC’20] addressed this problem by designing specialized VC solutions that however require the HE scheme to work with very specific parameters; notably HE ciphertexts must be over \(\mathbb {Z}_q\) for a large prime q.

Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme

Abstract: Homomorphic encryption is an emerging form of encryption that provides the ability to compute on encrypted data without ever decrypting them. Potential applications include aggregating sensitive encrypted data on a cloud environment and computing on the data in the cloud without compromising data privacy. There have been several recent advances resulting in new homomorphic encryption schemes and optimized variants. We implement and evaluate the performance of two optimized variants, namely Bajard-Eynard-Hasan-Zucca (BEHZ) and Halevi-Polyakov-Shoup (HPS), of the most promising homomorphic encryption scheme in CPU and GPU. The most interesting (and also unexpected) result of our performance evaluation is that the HPS variant in practice scales significantly better (typically by 15-30 percent) with increase in multiplicative depth of the computation circuit than BEHZ, implying that the HPS variant will always outperform BEHZ for most practical applications. For the multiplicative depth of 98, our fastest GPU implementation performs homomorphic multiplication in 51 ms for 128-bit security settings, which is faster by two orders of magnitude than prior results and already practical for cloud environments supporting GPU computations. Large multiplicative depths supported by our implementations are required for applications involving deep neural networks, logistic regression learning, and other important machine learning problems.

A Blockchain-Based Multi-Cloud Storage Data Auditing Scheme to Locate Faults

Abstract: Network storage services have benefited countless users worldwide due to the notable features of convenience, economy and high availability. Since a single service provider is not always reliable enough, more complex multi-cloud storage systems are developed for mitigating the data corruption risk. While a data auditing scheme is still needed in multi-cloud storage to help users confirm the integrity of their outsourced data. Unfortunately, most of the corresponding schemes rely on trusted institutions such as the centralized third-party auditor (TPA) and the cloud service organizer, and it is difficult to identify malicious service providers after service disputes. Therefore, we present a blockchain-based multi-cloud storage data auditing scheme to protect data integrity and accurately arbitrate service disputes. We not only introduce the blockchain to record the interactions among users, service providers, and organizers in data auditing process as evidence, but also employ the smart contract to detect service dispute, so as to enforce the untrusted organizer to honestly identify malicious service providers. We also use the blockchain network and homomorphic verifiable tags to achieve the low-cost batch verification without TPA. Theoretical analyses and experiments reveal that the scheme is effective in multi-cloud environments and the cost is acceptable.

An Efficient NIZK Scheme for Privacy-Preserving Transactions Over Account-Model Blockchain

Abstract: We introduce the abstract framework of decentralized smart contracts system with balance and transaction amount hiding property over account-model blockchain. To build a concrete system with such properties, we utilize a homomorphic public-key encryption scheme and construct a highly efficient non-interactive zero knowledge (NIZK) argument based upon the encryption scheme to ensure the validity of the transactions. Our NIZK scheme is perfect zero knowledge in the common reference string model, while its soundness holds in the random oracle model. Compared to previous similar constructions, our proposed NIZK argument dramatically improves the time efficiency in generating a proof, at the cost of relatively longer proof size.

Compressible FHE with Applications to PIR

Abstract: Homomorphic encryption (HE) is often viewed as impractical, both in communication and computation. Here we provide an additively homomorphic encryption scheme based on (ring) LWE with nearly optimal rate (\(1-\epsilon \) for any \(\epsilon >0\)). Moreover, we describe how to compress many Gentry-Sahai-Waters (GSW) ciphertexts (e.g., ciphertexts that may have come from a homomorphic evaluation) into (fewer) high-rate ciphertexts.

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Abstract: We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping procedure is more precise, more efficient (in terms of CPU cost and number of consumed levels), and is more reliable and 128-bit-secure. Unlike the previous approaches, it does not require the use of sparse secret-keys. Therefore, to the best of our knowledge, this is the first procedure that enables a highly efficient and precise bootstrapping with a low probability of failure for parameters that are 128-bit-secure under the most recent attacks on sparse R-LWE secrets.

Reliable and Privacy-Preserving Truth Discovery for Mobile Crowdsensing Systems

Abstract: Truth discovery has received considerable attention in mobile crowdsensing systems. In real practice, it is vital to resolve conflicts among a large amount of sensory data and estimate the truthful information. Although truth discovery has been widely explored to improve aggregation accuracy, numerous security and privacy issues still need to be addressed. Existing schemes either do not guarantee the privacy of each participating user, or fail to consider practical needs in crowdsensing systems. In this paper, we present two reliable and privacy-preserving truth discovery schemes for different scenarios. Our first design is fit for applications where users are relatively stable. By employing the homomorphic Paillier encryption, one-way hash chain, and super-increasing sequence techniques, this approach not only guarantees strong privacy, but also is highly efficient and practical. Our second design suits applications where users are frequently moving. In such an application, we explore data perturbation and homomorphic Paillier encryption to shift all user workloads to the server side, without compromising users’ privacy. Through detailed security analysis, we demonstrate that both schemes are secure, practical, and privacy-preserving. Moreover, extensive experiments based on real world and simulated mobile crowdsensing systems, we demonstrate the efficiency of our proposed schemes.

SoK: Fully Homomorphic Encryption Compilers

Abstract: Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development.In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools’ performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.

High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine Function.

Abstract: Approximate homomorphic encryption with the residue number system (RNS), called RNS-variant Cheon-Kim-Kim-Song (RNS-CKKS) scheme [12, 13], is a fully homomorphic encryption scheme that supports arithmetic operations for real or complex number data encrypted. Although the RNS-CKKS scheme is a fully homomorphic encryption scheme, most of the applications with the RNS-CKKS scheme use it as the only leveled homomorphic encryption scheme because of the lack of the practicality of the bootstrapping operation of the RNS-CKKS scheme. One of the crucial problems of the bootstrapping operation is its poor precision. While other basic homomorphic operations ensure sufficiently high precision for practical use, the bootstrapping operation only supports about 20-bit fixed-point precision at best, which is not high precision enough to be used for the reliable large-depth homomorphic computations until now.

Bootstrapping in FHEW-like Cryptosystems

Abstract: FHEW and TFHE are fully homomorphic encryption (FHE) cryptosystems that can evaluate arbitrary Boolean circuits on encrypted data by bootstrapping after each gate evaluation. The FHEW cryptosystem was originally designed based on standard (Ring, circular secure) LWE assumptions, and its initial implementation was able to run bootstrapping in less than 1 second. The TFHE cryptosystem used somewhat stronger assumptions, such as (Ring, circular secure) LWE over the torus with binary secret distribution, and applied several other optimizations to reduce the bootstrapping runtime to less than 0.1 second. Up to now, the gap between the underlying security assumptions prevented a fair comparison of the cryptosystems for the same security settings. We present a unified framework that includes the original and extended variants of both FHEW and TFHE cryptosystems, and implement it in the open-source PALISADE lattice cryptography library using modular arithmetic. Our analysis shows that the main distinction between the cryptosystems is the bootstrapping procedure used: Alperin-Sherif--Peikert (AP) for FHEW vs. Gama--Izabachene--Nguyen--Xie (GINX) for TFHE. All other algorithmic optimizations in TFHE equally apply to both cryptosystems. The GINX bootstrapping method makes essential the use of binary secrets, and cannot be directly applied to other secret distributions. In the process of comparing the two schemes, we present a simple, lightweight method to extend GINX bootstrapping (e.g., as employed by TFHE) to ternary uniform and Gaussian secret distributions, which are included in the HE community security standard. Our comparison of the AP and GINX bootstrapping methods for different secret distributions suggests that the TFHE/GINX cryptosystem provides better performance for binary and ternary secrets while FHEW/AP is faster for Gaussian secrets. We make a recommendation to consider the variants of FHEW and TFHE cryptosystems based on ternary and Gaussian secrets for standardization by the HE community.

Cloud-Based Quadratic Optimization With Partially Homomorphic Encryption

Abstract: This article develops a cloud-based protocol for a constrained quadratic optimization problem involving multiple parties, each holding private data. The protocol is based on the projected gradient ascent on the Lagrange dual problem and exploits partially homomorphic encryption and secure communication techniques. Using formal cryptographic definitions of indistinguishability, the protocol is shown to achieve computational privacy. We show the implementation results of the protocol and discuss its computational and communication complexity. We conclude this article with a discussion on privacy notions.

PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic Encryption

Abstract: Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without decryption, allowing us to evaluate arithmetic functions efficiently on the CKKS side, and to evaluate look-up tables on FHEW ciphertexts. Our FHEW → CKKS conversion algorithm is more practical than the existing methods. We improve the computational complexity from linear to sublinear. Moreover, the size of our conversion key is significantly smaller, e.g., reduced from 80 gigabytes to 12 megabytes. We present extensive benchmarks of PEGASUS, including sigmoid/ReLU/min/max/division, sorting and max-pooling. To further demonstrate the capability of PEGASUS, we developed two more applications. The first one is a private decision tree evaluation whose communication cost is about two orders of magnitude smaller than the previous HE-based approaches. The second one is a secure K-means clustering that is able to run on thousands of encrypted samples in minutes that outperforms the best existing system by 14 × – 20×. To the best of our knowledge, this is the first work that supports practical K-means clustering using HE in a single server setting.

Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.

Abstract: Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations. Existing approaches to sharing of distributed medical data either provide only limited protection of patients’ privacy or sacrifice the accuracy of results. Here, the authors propose a federated analytics system, based on multiparty homomorphic encryption (MHE), to overcome these issues.

Accelerating Fully Homomorphic Encryption Through Architecture-Centric Analysis and Optimization

Abstract: Homomorphic Encryption (HE) has drawn significant attention as a privacy-preserving approach for cloud computing because it allows computation on encrypted messages called ciphertexts. Among the numerous HE schemes proposed thus far, HE for Arithmetic of Approximate Numbers (HEAAN) is rapidly gaining in popularity across a wide range of applications, as it supports messages that can tolerate approximate computations with no limit on the number of arithmetic operations applicable to the ciphertexts. A critical shortcoming of HE is the high computation complexity of ciphertext arithmetic; specifically, HE multiplication (HE Mul) is more than 10,000 times slower than the corresponding multiplication between unencrypted messages. This has led to a large body of HE acceleration studies, including those that exploit FPGAs; however, a rigorous analysis of the computational complexity and data access patterns of HE Mul is lacking. Moreover, the proposals mostly focused on designs with small parameter sizes, making it difficult accurately to estimate the performance of the HE accelerators when conducting a series of complex arithmetic operations. In this paper, we first describe how HE Mul of HEAAN is performed in a manner friendly to non-crypto experts. Then, we conduct a disciplined analysis of its computational and memory-access characteristics, through which we (1) extract parallelism in the key functions composing HE Mul and (2) demonstrate how to map the parallelism effectively to popular parallel processing platforms, CPUs and GPUs, by applying a series of optimizations such as transposing matrices and pinning data to threads. This leads to performance improvements of HE Mul on a CPU and a GPU by $2.06\times $ and $4.05\times $ , respectively, over the reference HEAAN running on a CPU with 24 threads.

Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52

Abstract: Modern implementations of homomorphic encryption (HE) rely heavily on polynomial arithmetic over a finite field. This is particularly true of the BGV, BFV, and CKKS HE schemes. Two of the biggest performance bottlenecks in HE primitives and applications are polynomial modular multiplication and the forward and inverse number-theoretic transform (NTT). Here, we introduce Intel® Homomorphic Encryption Acceleration Library (Intel® HEXL), a C++ library which provides optimized implementations of polynomial arithmetic for Intel® processors. Intel HEXL takes advantage of the recent Intel® Advanced Vector Extensions 512 (Intel® AVX512) instruction set to provide state-of-the-art implementations of the NTT and modular multiplication, measuring up to 7.2x single-threaded speedup over a native C++ baseline. Intel HEXL is available open-source at https://github.com/intel/hexl under the Apache 2.0 license and has been adopted by the Microsoft SEAL and PALISADE homomorphic encryption libraries

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

Abstract: Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

Efficient number theoretic transform implementation on GPU for homomorphic encryption

Abstract: Lattice-based cryptography forms the mathematical basis for current homomorphic encryption schemes, which allows computation directly on encrypted data. Homomorphic encryption enables privacy-preserving applications such as secure cloud computing; yet, its practical applications suffer from the high computational complexity of homomorphic operations. Fast implementations of the homomorphic encryption schemes heavily depend on efficient polynomial arithmetic, multiplication of very large degree polynomials over polynomial rings, in particular. Number theoretic transform (NTT) accelerates large polynomial multiplication significantly, and therefore, it is the core arithmetic operation in the majority of homomorphic encryption scheme implementations. Therefore, practical homomorphic applications require efficient and fast implementations of NTT in different computing platforms. In this work, we present an efficient and fast implementation of NTT, inverse NTT and NTT-based polynomial multiplication operations for GPU platforms. To demonstrate that our GPU implementation can be utilized as an actual accelerator, we experimented with the key generation, the encryption and the decryption operations of the Brakerski/Fan–Vercauteren (BFV) homomorphic encryption scheme implemented in Microsoft’s SEAL homomorphic encryption library on GPU, all of which heavily depend on the NTT-based polynomial multiplication. Our GPU implementations improve the performance of these three BFV operations by up to 141.95 $$\times$$ , 105.17 $$\times$$ and 90.13 $$\times$$ , respectively, on Tesla v100 GPU compared to the highly optimized SEAL library running on an Intel i9-7900X CPU.

PLVA: Privacy-Preserving and Lightweight V2I Authentication Protocol

Abstract: Vehicular ad hoc networks (VANETs) significantly improves the efficiency and safety of driving since it reduces traffic jams and avoiding accidents, in which the necessary security goals are guaranteed using cryptographic method. In reality, the computation efficiency is very important in implementing the protocol in VANETs. When a vehicle with high speed enters in the coverage of a roadside unit (RSU), the computation overhead of authentication not only affects the communication experience, but also downgrades the driving safety. The feasible solution is to share a message in advance between vehicle and RSU with the help of certification authority (CA), however, CA can deduce the vehicle's route that should be privacy. In this paper, a privacy-preserving and lightweight V2I authentication (PLVA) protocol is proposed. Specifically, in the beginning phase, all roadside units in a region are converted to a vector using the Moore curve technique, then, a vehicle deduces the RSUs' information on its planning route using BGN homomorphic encryption before the vehicle begins its trip, meanwhile, CA knows nothing about the route plan although it assists the above process. With the deduced RSUs' information, fast authentication is achieved between vehicle and each RSU on its route. Moreover, performance evaluation illustrates that our PLVA is efficient in practical VANETs environment.

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Abstract: Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...

Privacy-Preserving Multidimensional Data Aggregation Scheme Without Trusted Authority in Smart Grid

Abstract: Privacy-preserving multidimensional data aggregation is a significant basic building block for protecting the users’ privacy in smart grid, and it can not only expand the applications of data aggregation but also fulfill the demands of the fine-grained analysis of multidimensional data. However, traditional multidimensional data aggregation schemes depend on the trusted authority and cannot resist the coalition attack from the gateway (GW) and the control center (CC), which may cause the users’ fears about privacy violations. Therefore, this article proposes a privacy-preserving multidimensional data aggregation scheme without trusted authority in smart grid based on the ElGamal homomorphic cryptosystem with distributed decryption, which can resist the coalition attack from the GW and the CC. What is more, the proposed scheme does not depend on the trusted authority which is not fully trusted in the real world. The detailed security analysis indicates that our scheme can satisfy the security requirement of smart grid. The performance analysis shows that the proposed scheme achieves the lowest computation and communication costs in data encryption phase and data aggregation phase, thus it is appropriate for many practical applications.

Efficient Homomorphic Conversion Between (Ring) LWE Ciphertexts

Abstract: In the past few years, significant progress on homomorphic encryption (HE) has been made toward both theory and practice. The most promising HE schemes are based on the hardness of the Learning With Errors (LWE) problem or its ring variant (RLWE). In this work, we present new conversion algorithms that switch between different (R)LWE-based HE schemes to take advantage of them. Specifically, we present and combine three ideas to improve the key-switching procedure between LWE ciphertexts, transformation from LWE to RLWE, as well as packing of multiple LWE ciphertexts in a single RLWE encryption. Finally, we demonstrate an application of building a secure channel between a client and a cloud server with lightweight encryption, low communication cost, and capability of homomorphic computation.

EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

Abstract: Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

A Privacy-Preserving Homomorphic Scheme With Multiple Dimensions and Fault Tolerance for Metering Data Aggregation in Smart Grid

Abstract: Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP3) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.