Homomorphic Encryption Techniques Research Articles

An Enhanced Methodology on Internet of Things with Cloud in Smart Electrical Systems

The smart management system plays a vital role in many domains and improves the reliability of protection and privacy of a system. Electrical systems have become a part in everyday human life. The next generation electrical systems will entirely depends on fully automated and smart control systems. In the present paper various mechanisms of cloud gateways and security issues are explored for smart management of an electrical system. The present survey work is reconnoitred with Internet of Things (IoT) in association with cloud. Cloud based IoT in smart electrical system provides potential enhancement of performance, management, and resilience of the smart system. However, the espousal of cloud based IoT system in smart electrical system to store and retrieve the data from cloud may increase risks in data privacy and security. Despite the different flaws in global integration of cloud with IoT through internet, various end-to-end security schemes are discussed to overcome these flaws. As a result in many of the applications easy IoT cloud gateway along with homomorphic encryption technique is set up to solve communication overheads and security issues.

Evaluating Performance of Asymmetric Homomorphic Encryption Algorithms for Privacy Preservation in Location Based Services

The recent advancement in mobile technology & wireless communication has change the way of people’s communication & execution of tasks. In location based services (LBS), there are large number of LBS users who are available to get their location based information. In LBS, Users work together mutually to compute the centroid by per-forming large number of data aggregation operation that aggregate user’s location before sending it to the LBS provider. Users have to submit their personalized information to the LBS provider. Location privacy is the one of the most critical issue. Homomorphic encryption technique ensures the secure data aggregation by encrypting the user’s location using Homomorphic encryption algorithm. For privacy requirement, semantic security is a standard for any encryption schema. Many Homomorphic encryption algorithm are available, so it’s require to investigate the performance of those that are semantically secure. In this paper, we will discuss homomorphic encryption algorithm and also attempt to evaluate the performance of various additive asymmetric Homomorphic encryption algorithms. Our work is inspired to recognize an asymmetric homomorphic encryption algorithms for LBS that offers strongest location privacy.

Privacy Preserving Using Extended Euclidean Algorithm Applied To RSA-Homomorphic Encryption Technique

Communication of confidential information over Internet is the key aspect of security applications. Providing protection to sensitive information is of major concern. Many cryptographic algorithms have been in use for providing security of confidential information. Providing security for data has become major challenge in this era. Classical cryptography is playing a major role in providing security for applications. In modern days securing confidential information in the cloud is considered as an important challenge. Homomorphic Encryption technique is one of the best solutions that provide security in the cloud[1]. In this paper, Extended Euclidean Algorithm is used for generating keys. This technique follows RSA Homomorphic encryption technique. .RSA Homomorphic encryption using Extended Euclidean algorithm (RSA-HE-EEA) is secure when compared to RSA as it based on the generation of private key which makes the algorithm complex .This technique of using Extended Euclidean Algorithm(EEA) is fast and secure when compared to RSA homomorphic encryption technique. The encryption process utilizes modulo operator which gives security as well.The beauty of this algorithm is in generation of private key which uses Extended Euclidean Algorithm (EEA) that helps in avoiding brute force attacks. Also, this technique uses Homomorphic operations which gives enhance security to confidential information in the cloud.

Enhanced Homomorphic Encryption technique using RSA ALGORITHM with multiple keys

Enhanced Homomorphic Encryption technique using RSA ALGORITHM with multiple keys

Achieving Lightweight Verifiable Privacy Preserving Search Over Encrypted Data

When cloud clients outsource their database to the cloud, they entrust management operations to a cloud service provider who is expected to answer the client’s queries on the cloud where database is located. Efficient techniques can ensure critical requirements for outsourced data’s integrity and authenticity. A lightweight privacy preserving verifiable scheme for outsourcingdatabase securely is proposed, our scheme encrypts data before outsourcing and returned query results are verified with parameters of correctness and completeness. Our scheme is projected on lightweight homomorphic encryption technique and bloom filter which are efficiently authenticated to guarantee the outsourced database’s integrity, authenticity, and confidentiality. An ordering challenge technique is proposed for verifying top-k query results. We conclude by detailing our analysis of security proofs, privacy, verifiability and the performance efficiency of our scheme. Our proposed scheme’s proof and evaluation analysis show its security and efficiency for practical deployment. We also evaluate our scheme’s performances over two UCI data sets.

Ensuring Efficient Data Storage using Fully Mature Homomorphic Encryption Technique in the Cloud Environment

In cloud computing, user database is stored at remote site instead of user computer’s hard disk where the connection between remote site and user computer is provided by internet connection. As cloud computing essentially places data outside the custody of owner of data, it inexorably hosts security disputes. The distance among the physical and the client location of data generates a barrier as the data can be accessed by an unauthorized party and this would influence the solitude of client’s data. The utilization of traditional encryption systems to encrypt the data prior to transmitting to the cloud provider has been most extensively utilized technique to link this security gap. Be that as it may, the customer will require offering the private key to the server to unscramble the information in front of playing out the figuring’s fundamental. Homomorphic encryption techniques permits computations on encrypted data devoid of decryption. This paper deals with the utilization of Fully Mature Homomorphic Encryption (FMHE) to encode the client’s data on cloud server and as well it facilitates to perform required computations on the encrypted data

Secure and Differentially Private Logistic Regression for Horizontally Distributed Data

Scientific collaborations benefit from sharing information and data from distributed sources, but protecting privacy is a major concern. Researchers, funders, and the public in general are getting increasingly worried about the potential leakage of private data. Advanced security methods have been developed to protect the storage and computation of sensitive data in a distributed setting. However, they do not protect against information leakage from the outcomes of data analyses. To address this aspect, studies on differential privacy (a state-of-the-art privacy protection framework) demonstrated encouraging results, but most of them do not apply to distributed scenarios. Combining security and privacy methodologies is a natural way to tackle the problem, but naive solutions may lead to poor analytical performance. In this paper, we introduce a novel strategy that combines differential privacy methods and homomorphic encryption techniques to achieve the best of both worlds. Using logistic regression (a popular model in biomedicine), we demonstrated the practicability of building secure and privacy-preserving models with high efficiency (less than 3 min) and good accuracy [<1% of difference in the area under the receiver operating characteristic curve (AUC) against the global model] using a few real-world datasets.

A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side

BackgroundSingle Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting one’s genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both.ResultsIn this paper, we propose a secure SNP panel scheme using homomorphically encrypted K-mers without requiring SNP calling on the user side and without revealing the panel information to the user. Use of the powerful homomorphic encryption technique is desirable, but there is no known algorithm to efficiently align two homomorphically encrypted sequences. Thus, we designed and implemented a novel secure SNP panel scheme utilizing the computationally feasible equality test on two homomorphically encrypted K-mers. To make the scheme work correctly, in addition to SNPs in the panel, sequence variations at the population level should be addressed. We designed a concept of Point Deviation Tolerance (PDT) level to address the false positives and false negatives. Using the TCGA BRCA dataset, we demonstrated that our scheme works at the level of over a hundred thousand somatic mutations. In addition, we provide a computational guideline for the panel design, including the size of K-mer and the number of SNPs.ConclusionsThe proposed method is the first of its kind to protect both the user’s sequence and the hospital’s panel information using the powerful homomorphic encryption scheme. We demonstrated that the scheme works with a simulated dataset and the TCGA BRCA dataset. In this study, we have shown only the feasibility of the proposed scheme and much more efforts should be done to make the scheme usable for clinical use.

A New Communication-Efficient Privacy-Preserving Range Query Scheme in Fog-Enhanced IoT

Fog-enhanced Internet of Things (IoT) has received considerable attention in recent years, as fog devices deployed at the network edge can not only improve the performance of IoT applications but also enhance the security and privacy of IoT. In this paper, we present a new communication-efficient privacy-preserving range query scheme in Fog-enhanced IoT. With the proposed scheme, both the query range and individual IoT device's data can be privacy-preserved by using BGN homomorphic encryption technique. In addition, the proposed scheme employs a range query expression, decomposition, and composition technique to reorganize the range query, which can achieve O(√n) communication efficiency. Detailed security analysis shows that the proposed scheme is really a privacy-preserving range query scheme. Extensive experiments are conducted, and the results indicate that the proposed scheme is also efficient in terms of low range query generation cost and low communication overhead.

Achieving Efficient and Privacy-Preserving k-NN Query for Outsourced eHealthcare Data.

The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as [Formula: see text], where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with [Formula: see text] computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost.

A privacy protection-oriented parallel fully homomorphic encryption algorithm in cyber physical systems

Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) technique to realize the efficient operation of ciphertext without decryption. However, most current homomorphic encryption algorithms only support limited data types, making it difficult to be widely applied in actual environment. To address this limitation, we propose a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm not only expands the data types supported by the existing fully homomorphic encryption algorithms, but also utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speed-up exceeding 5, which not only solves the limited application problem of the existing fully homomorphic encryption algorithm, but also meets the requirements for the efficient homomorphic encryption of floating-point numbers in cloud computing environment.

Achieving Efficient and Privacy-Preserving Multi-Keyword Conjunctive Query Over Cloud

With the explosive growth of data, it has become increasingly popular to deploy the powerful cloud to manage data. Meanwhile, as the cloud is not always fully trusted, personal and sensitive data have to be encrypted before being outsourced to the cloud. Naturally, this brings a serious challenge for the cloud to provide secure and efficient query services over huge volumes of data. Although existing works have proposed some solutions to solve the above challenge, most of them just focus on the single keyword query and cannot directly support multi-keyword query. Even though some works have discussed solutions for the multi-keyword query, they cannot well balance the efficiency and privacy. Therefore, in this paper, we propose a novel multi-keyword conjunctive query scheme over cloud, which can achieve high query efficiency with small privacy leakage. In specific, we first design a tree-based index to support the multi-keyword conjunctive query and employ Boneh-Goh-Nissim (BGN) homomorphic encryption technique to protect its privacy. Then, based on the tree-based index, we propose a wildcard search algorithm to improve its query efficiency. Finally, the detailed security analysis shows that the proposed scheme is really privacy-preserving, and extensive simulation results also demonstrate its efficient.

Adaptive optimal multi key based encryption for digital image security

SummaryThe security of digital images is an essential and challenging task on shared communication Model. Generally, high secure working environment and data are also secured with an encryption and decryption method by using secret and public keys. In this paper, the innovative encryption technique for image security, ie, Multiple key‐based Homomorphic Encryption (MHE) technique is proposed. For increasing the security level of encryption and decryption processes, the optimal key is selected using Adaptive Whale Optimization (AWO) algorithm. Fitness function was considered for optimization as PSNR of plain and cipher images. The original image was transformed into blocks and then rearranged utilizing encryption process, this work achieved maximum security, much better than other encryption techniques. From the outcomes, one can achieve incredible quality of the proposed model, the maximum PSNR, and the minimum MSE contrasted with other encryption schemes.

RETRACTED ARTICLE: Secret image sharing scheme with encrypted shadow images using optimal homomorphic encryption technique

Secret Image Sharing (SIS) scheme is to encrypt a secret image into ‘n’ specious shadows. It is unable to reveal any data on the secret image if at least one of the shadows is not achieved. In this paper, wavelet-based secret image sharing scheme is proposed with encrypted shadow images using optimal Homomorphic Encryption (HE) technique. Initially, Discrete Wavelet Transform (DWT) is applied on the secret image to produce sub bands. From this process, multiple shadows are created, encrypted and decrypted for each shadow. The encrypted shadow can be recovered just by choosing some subset of these ‘n’ shadows that makes transparent and stack over each other. To improve the shadow security, each shadow is encrypted and decrypted using HE technique. For the concern on image quality, the new Oppositional based Harmony Search (OHS) algorithm was utilized to generate the optimal key. From the analysis, it shows that the proposed scheme provide greater security compared to other existing schemes.

A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications.

Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet’s payload. The scheme uses homomorphic encryption techniques to conceal the report’s measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet’s size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.

ClaMPP: a cloud-based multi-party privacy preserving classification scheme for distributed applications

Privacy preservation in a distributed environment is a challenging task as it requires efficient control strategies for authentication and integrity preservation of various users and applications. In a distributed environment, most of the data items are distributed among various parties which may be located across different geographical locations. In the literature, most of the techniques proposed for privacy preserving consider only two parties collaboration for data items sharing using data perturbation and homomorphic encryption technique. Since data perturbation is less accurate and homomorphic encryption incurs high computation cost, so to fill these gaps, this paper proposes a Cloud-based Multi-party Privacy Preserving Classification Scheme (ClaMPP), which uses Naive Bayesian Classifier using multi-party random masking and polynomial aggregation technique for privacy preservation among different parties. The proposed ClaMPP scheme consists of four protocols which are described as follows. Protocols I and II are used to calculate conditional probabilities between the item vectors in a secure manner by using Privacy Preserving Conditional Probability Protocol. In the protocol III, the prior probabilities between the item vectors are computed by using additive property of Paillier homomorphic encryption technique. The naive Bayesian classification model constructed from the calculated values from protocols I, II and III is used in protocol IV to generate the predictions for items based on user’s interest. Three datasets MovieLens100K, MovieLens20M and Jester are used in ClaMPP to analyse the privacy, accuracy and overhead costs incurred. It has been experimentally demonstrated that the proposed scheme is secure, and the privacy preservation does not affect the accuracy of the prediction. Comparative analysis of ClaMPP has been done with other related schemes based on off-line model computation cost. It has been demonstrated from the results obtained that computation cost decreases significantly in comparison with the other state-of-art techniques.

LPTD: Achieving lightweight and privacy-preserving truth discovery in CIoT

In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices’ privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.

Analyzing and Evaluating Efficient Privacy-Preserving Localization for Pervasive Computing

Privacy-preserving localization in crowdsourcing has drawn much attention recently. Under the classical nonadjacent subtraction-based localization (NSL) model, existing solutions based on homomorphic encryption techniques are of high computational and communication overheads. In this paper, an adjacent subtraction-based localization (ASL) model is first proposed. Then, an efficient privacy-preserving localization (EPPL) algorithm is developed under ASL without using any homomorphic encryption technique. In terms of the correctness, privacy, and efficiency, a comprehensive analysis is presented to investigate EPPL’s performance. Furthermore, the statistical equivalence between ASL and NSL is proved through the fact that the difference between their average location estimation results converges toward zero. The lower and upper bounds of the localization error are also derived for ASL under a bounded noise model. Extensive simulations are conducted to illustrate the equivalence between ASL and NSL, and the performance of EPPL regarding the correctness, privacy, and efficiency.

Speedy Cloud-RSA homomorphic scheme for preserving data confidentiality in cloud computing

Homomorphic encryption (HE) technique provides a third party with the ability to execute operations on ciphertexts. This property can be considered as a good solution to overcome some barriers limiting the widespread adoption of cloud services. However, since cloud environments are more threatened by security attacks and since cloud consumers often access to cloud services using resource-constrained devices, the HE schemes have to be promoted at security level and at running time to work efficiently. In El Makkaoui et al. (Cloud-RSA: an enhanced homomorphic encryption scheme. In: Rocha A, Serrhini M, Felgueiras C (eds) Europe and MENA cooperation advances in information and communication technologies. Advances in intelligent systems and computing, vol 520. Springer, Cham, pp 471–480, 2017b), we boosted RSA scheme at security level, Cloud-RSA. In this paper, we suggest two variants of Cloud-RSA scheme in order to speed up its decryption process. The variants are based on modifying the form of Cloud-RSA modulus and exponents. The first variant uses a modulus formed of two or more distinct primes and employs the Chinese remainder theorem (CRT) to encrypt and decrypt. Whereas, the second variant uses a modulus of the form $$n=p^rq^s$$ for $$r\ge 2$$ and $$s \ge 1$$ , and applies the Hensel lifting and the CRT to decrypt. Theoretical and simulation results show that the proposed variants provide a large decryption speedup over Cloud-RSA.

Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.

BackgroundLearning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis.ObjectiveThe goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression).MethodsWe adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques.ResultsUsing real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset.ConclusionsWe present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.