Encrypted Form Research Articles

Innovative solution for SARS-COV2 epidemic management

The SARS-CoV-2 pandemic challenged the authorities into taking measures that limit the spread of the virus in the community. TAMEC project (Advanced Community Approaches to Epidemic Management) developed a system that detects the contacts of patients infected with the SARS-CoV-2 virus and alerts them using mobile applications. It allows real-time analysis of confirmed patients and identified contacts, early detection of new outbreaks, and provides decisionmaking information to prevent viral transmission. The system uses its patent for homomorphic encryption, a method that allows to process and manipulate data in an encrypted format, without having access to the original data. Thus, the solution proposed in the TAMEC project fully respects the privacy rules of citizens imposed by the EU, using the concept of privacy preservation. Our solution offers the possibility to identify and validate a risk score that could become an extremely helpful tool in the stratification of COVID-19 patients. The TAMEC system is innovative in its simplicity and ability to facilitate the prevention of the spread of the SARS-CoV-2 virus in the community.

Multi-Authority Criteria-Based Encryption Scheme for IoT

Currently, the Internet of Things (IoT) provides individuals with real-time data processing and efficient data transmission services, relying on extensive edge infrastructures. However, those infrastructures may disclose sensitive information of consumers without authorization, which makes data access control to be widely researched. Ciphertext-policy attribute-based encryption (CP-ABE) is regarded as an effective cryptography tool for providing users with a fine-grained access policy. In prior ABE schemes, the attribute universe is only managed by a single trusted central authority (CA), which leads to a reduction in security and efficiency. In addition, all attributes are considered equally important in the access policy. Consequently, the access policy cannot be expressed flexibly. In this paper, we propose two schemes with a new form of encryption named multi-authority criteria-based encryption (CE) scheme. In this context, the schemes express each criterion as a polynomial and have a weight on it. Unlike ABE schemes, the decryption will succeed if and only if a user satisfies the access policy and the weight exceeds the threshold. The proposed schemes are proved to be secure under the decisional bilinear Diffie–Hellman exponent assumption (q-BDHE) in the standard model. Finally, we provide an implementation of our works, and the simulation results indicate that our schemes are highly efficient.

Secure And Dynamic Multi Keyword Ranked Search

Nowadays, information storing in third party storage is increased. Outsourcing the data to other storage device or servers which may questioned to the secure environment. However, sensitive data like medical information should need an privacy when it is stored in cloud storage. In this paper, a secure keyword search which provide the resultant data in a encrypted form where the end user can decrypt using the key given to them. It uses the Blowfish to encrypt the data and it also supports the data owner to delete or modify the content of their document. It also ensure accurate relevance score calculation between encrypted index and query vectors.

Secure and Dynamic Multi-Keyword Ranked Search

Nowadays, information storing in third party storage is increased. Outsourcing the data to other storage device or servers which may questioned to the secure environment. However, sensitive data like medical information should need an privacy when it is stored in cloud storage. In this paper, a secure keyword search which provide the resultant data in a encrypted form where the end user can decrypt using the key given to them. It uses the Blowfish to encrypt the data and it also supports the data owner to delete or modify the content of their document. It also ensure accurate relevance score calculation between encrypted index and query vectors.

Defending Virtual Gross System from Software GUSTY

The aim of our project is to build a secure Web Application platform for auditing. The auditee uploads their financial records to the platform and the uploaded records can only be accessed by an authorized auditor. The data integrity is ensured by using cryptography. We can prevent malware like Trojan. The data will be in an encrypted format. Even if a hacker manages to get his hands on the data, it cannot be exploited.

Tokenization of educational assets based on blockchain technologies

Possible scenarios for using blockchain technology in the field of education are considered. Methods and technologies of tokenization of assets, related to the educational process, are investigated. It is concluded, that the blockchain technology is decentralized and transparent with a high degree of reliability, which ensures the equality of all users of the chain's services. The transparency of the technology guarantees the participants in the process against abuse and forgery of documents. The study of the features of smart contracts made it possible to form the advantages of smart contracts in the field of education. This is, first of all, the conclusion of agreements without the participation of third parties, as well as the security and confidentiality of agreements. This ensures that the terms and subject of the agreement are kept secret, and that no one else can amend the agreement. At the same time, storing the contract in encrypted form ensures its confidentiality. A decrease in the cost of operations was noted. Tokenization of educational assets is considered on the example of preparing and defending a thesis with the subsequent registration of a diploma. The processes of passing the thesis in the context of using blockchain technology and issuing smart contracts are considered in detail. The advantages of using blockchain technology and smart contracts are illustrated with specific examples.
 A study of the means and mechanisms for ensuring the confirmation of the authenticity of educational documents, the confidentiality of students' personal cards, and student identification has been carried out.
 To create a decentralized distributed ledger for tokenization of educational assets, it is proposed to use blockchain technology and smart contracts based on the Ethereum platform

Secure File Storage & Sharing on Cloud Using Cryptography

In today’s world, simply having the capacity to transfer a file from one location to another isn’t enough. Businesses today face multiple security threats and a highly competitive environment. So they need a secure file transfer system to protect and reliably transfer their sensitive, business-critical data. Secure file transfer is a method of data sharing via a secure, reliable delivery method. Also, we use this between a client and a server. Cryptography is a technique that we use for securing information and communication in the presence of third parties. We use this technique to ensure that only those persons to whom the information is intended can read this. By using cryptography, we can prevent unauthorized users from accessing the information which is shared privately. In this paper, the plan proposed is to overcome the issues regarding the data that are being stored by the users on the cloud should be encrypted rather than storing them in a plain form such that the data will be protected from the attackers who are trying to read, delete or manipulate the data. Our application is focused on securely authenticating the user, before storing and sharing files, To create an application that lets a user encrypt and decrypt any type of file without any changes in the size during encryption & decryption, store every user data in the encrypted form on the cloud, to provide a communication medium between users via the chat application, to give direct access to the file for CRUD operation only to the owner.

Fine-Grained Attribute-Based Multikeyword Search for Shared Multiowner in Internet of Things

At present, with the popularity of Internet of things (IoT), a huge number of datasets generated by IoT devices are being uploaded to the cloud storage in remote data management service, but a series of security and privacy defects also arises, where one of the best ways for preventing data disclosure is encryption. Among them, searchable encryption (SE) is considered to be a very attractive cryptographic technology, since it allows users to search records in an encrypted form and to protect user’s data on an untrusted server. For the sake of enhancing search permission, attribute-based keyword search (ABKS) is an efficient method to provide secure search queries and fine-grained access authentications over ciphertexts. However, most existing ABKS schemes concentrate on single keyword search, which usually returns redundant and irrelevant results, so it would cost some unnecessary computation and communication resources. Furthermore, existing work in the literature mostly only supports unshared multiowner where a specific data owner owns each file, which is not able to satisfy more desired expressive search. In this work, we propose a novel attribute-based multikeyword search for shared multiowner (ABMKS-SM) primitive in IoT to achieve enhanced access control for users; meanwhile, it can support multikeyword search over ciphertexts and give a formal security analysis in the adaptive against chosen keyword attack (IND-CKA) model. Finally, we have also implemented this prototype to show efficiency when compared with some previous schemes.

Addressing Class Imbalance in Federated Learning

Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution and quantity of the training data on the clients' side may lead to significant challenges such as class imbalance and non-IID (non-independent and identically distributed) data, which could greatly impact the performance of the common model. While much effort has been devoted to helping FL models converge when encountering non-IID data, the imbalance issue has not been sufficiently addressed. In particular, as FL training is executed by exchanging gradients in an encrypted form, the training data is not completely observable to either clients or server, and previous methods for class imbalance do not perform well for FL. Therefore, it is crucial to design new methods for detecting class imbalance in FL and mitigating its impact. In this work, we propose a monitoring scheme that can infer the composition of training data for each FL round, and design a new loss function -- Ratio Loss to mitigate the impact of the imbalance. Our experiments demonstrate the importance of acknowledging class imbalance and taking measures as early as possible in FL training, and the effectiveness of our method in mitigating the impact. Our method is shown to significantly outperform previous methods, while maintaining client privacy.

A High Secured Ecc Based Iot Audiometric System For Cloud Based Dynamic Sensor Domain Environment

This paper is made based on the requirement of different domain users for effective hearing system. The system uses different devices to enhance the audio capability of different users at different application environment. The system uses IoT devices, sensors and different gateways for effective use of IoT based systems. The system will enhance the hearing capability of all users at different conference or personal or professional environments for the effective hearing capability among different hearing capabilities. It provides the environment to increase or decrease the volume of the source system based on the hearing capability of participants in the hearing environment. It can be used in any domain like conferences, home environment, education institutions and at public locations. It also adopts a security algorithm to protect the customer’s data in encrypted format at cloud system. It recognizes the candidate at any time based on the ID assigned to an individual.

Lightweight Public Key Authenticated Encryption With Keyword Search Against Adaptively-Chosen-Targets Adversaries for Mobile Devices

Cloud storage services have grown extensively in recent years. For security and privacy purposes, sensitive data need to be outsourced to clouds in encrypted form. Searchable public key encryption (SPKE) enables data ciphertexts to be retrieved by keyword(s) without decryption. Unfortunately, most of the existing SPKE schemes cannot withstand the keyword guessing attack. To combat such attack, public key authenticated encryption with keyword search (PAEKS) was presented. However, the existing PAEKS schemes were proven secure under a designated-targets security model, in which an adversary only can attack a sender and a recipient designated by the challenger. Our cryptanalysis indicates that such a scheme may be insecure against the practical attacks where the adversaries choose their targets by themselves. To fight against adaptively-chosen-targets adversaries, we refine the adversary model for PAEKS by permitting the adversaries to choose their targets adaptively, and then formalize the security definitions under the improved security model. After that, we devise a lightweight PAEKS scheme that avoids the time-consuming bilinear pairing operations and give the security proofs. The comparisons show that it outperforms the existing bilinear pairing-based PAEKS schemes in both the computation and communication performance, and therefore is more suitable for the resource-constrained mobile devices.

Steganographic Techniques Using Modified Least Significant Bit and Modification Reshape Transposition Methods

A message is a form of conveying information. Various ways are used to secure the information conveyed in the form of messages either in encrypted form or in the form of applying a password in the message. Messages can also be encrypted and embedded in other media such as images (steganography). This research aimed to insert a message into the form of an image by combining the Modified Least Significant Bit (MLSB) method in encrypting messages and reshape modification technique to determine at which position the message encryption will be embedded in the image. Tests were carried out to obtain the quality of the encryption process using the parameters of Fidelity, mean square error, peak signal to noise ratio, testing on file type, robustness, and comparison of message contents. The results of the tests showed that the files that can be used are files with the image file type in the lossless compression category, the rotation can be done at 90, 180, 270 without destroying the message in it, and changing the pixel in the image file will destroy the message inside

Decentralised Artificial Intelligence Enabled Blockchain Network Model

Blockchain and Artificial intelligence are novel technologies which take prominent place across all industries. AI refers to intelligent tasks that are carried out by machines which in-turn are done by humans in olden days. Blockchain a collection of decentralised networks which revolutionizes and upgrades the business operations. Blockchain creates a decentralised environment that shares data which is in encrypted form between ledgers in a confidential way without the involvement of any third party. Blockchain and Artificial Intelligence has been shaping their paths with a slight overlap of their own. Combined decentralised AI networks enables the businesses to take decisions without the need of centralised control activity. Block chain consists of nodes and it follows distributed ledger technology; Proof of Work (PoW) consensus algorithm used in blockchain makes use of lot of computing power and energy for the miners to get reward. To overcome this difficulty, this paper proposes AI enabled Miner Node Selection Algorithm in Block chain Networks based on PoW consensus algorithm. The paper presents a miner node selection with PoW in blockchain which reduce data storage in blockchain by data pruning technique. By executing this algorithm in blockchain network, unbiased blockchain implementation will be easier. The integration of AI with the implementation of blockchain enhances the efficiency of blockchain network by reducing the computational power, energy and time spent in selection of the node which is evident in our experimental results.

Acceleration of Inner-Pairing Product Operation for Secure Biometric Verification.

With the recent advances in mobile technologies, biometric verification is being adopted in many smart devices as a means for authenticating their owners. As biometric data leakage may cause stringent privacy issues, many proposals have been offered to guarantee the security of stored biometric data, i.e., biometric template. One of the most promising solutions is the use of a remote server that stores the template in an encrypted form and performs a biometric comparison on the ciphertext domain, using recently proposed functional encryption (FE) techniques. However, the drawback of this approach is that considerable computation is required for the inner-pairing product operation used for the decryption procedure of the underlying FE, which is performed in the authentication phase. In this paper, we propose an enhanced method to accelerate the inner-pairing product computation and apply it to expedite the decryption operation of FE and for faster remote biometric verification. The following two important observations are the basis for our improvement—one of the two arguments for the decryption operation does not frequently change over authentication sessions, and we only need to evaluate the product of multiple pairings, rather than individual pairings. From the results of our experiments, the proposed method reduces the time required to compute an inner-pairing product by 30.7%, compared to the previous best method. With this improvement, the time required for biometric verification is expected to decrease by up to 10.0%, compared to a naive method.

IMPLEMENTATION OF PRIVACY PRESERVING AND DYNAMIC SEARCHING MECHANISM WITH BIOMETRIC AUTHENTICATION IN CLOUD STORAGE

In the recent times, cloud storage tends to be a primary storage means for external data. Cloud defense of the data against attacks is the main challenge. Private or semi-private information growth has rapidly expanded over the information network; privacy safeguards have failed to address the search mechanisms. In the field of information networks, privacy protection is an important factor in carrying out various data mining operations with encrypted data stored in different storage systems. A tolerance and protection against data corruption mechanism should be developed which is difficult to achieve. Furthermore, as there is no adequate audit mechanism, the integrity of the stored data become questionable. In addition to this, the user authentication is another challenge. The current solution provides only a remote audit mechanism. It requires data owners to always remain online so that the auditing process is manually handled, which is sometimes unworkable. In this paper, we propose a new, regenerative, public audit methodology accompanied by third-party audits. The existing data search system provides one solution that can be used to maintain the confidentiality of indexing. Documents are stored on a private server in plain word form, which compromise the protection of privacy. So that this system is improved to make the document more secure and efficient, we first store the documents in encrypted form on server, and use the Key Distribution Center (KDC). To generate keys the KDC uses the user's biometric feature. In order to improve the search experience, we also implement TF-IDF, which provides an efficient evaluation of the results. Lastly, we carry out comprehensive data set experiments to evaluate our proposed system performance. Experimental results demonstrate that in terms of safeguarding the privacy, efficient and safe search for encrypted distributed documents the proposed system is better than existing. The methodology suggested also includes an auditing mechanism by third parties to ensure data integrity.

A Traitor-Resistant and Dynamic Anonymous Communication Service for Cloud-Based VANETs

Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this article, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs, which supports several advantageous features. In TD-ACF, each vehicle is represented by a set of attributes instead of its real identity, and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved in this way. Meanwhile, TD-ACF supports two practical requirements in cloud-based VANETs: the revocation and the traceability of traitor. For the former, TD-ACF can force a vehicle to exit the communication network at any moment. We employ an efficient binary tree algorithm to reduce the size of key updates for revocation from the traditional linear to the logarithmic level. For the latter, we overcome the barrier of the one-to-many relationship between a vehicle and the shared set of attributes to support traitor tracing. In TD-ACF, unlike most existing schemes, the Semi-Trusted Cloud (STC) can directly capture and punish a traitor instead of querying all the records in the list of unrevoked vehicles. In addition, we solve the key escrow problem that plagues most existing attribute-based schemes. The theoretical analysis and experimental simulation show that the proposed scheme is feasible and effective.

PGRide: Privacy-Preserving Group Ridesharing Matching in Online Ride Hailing Services

An online ride hailing (ORH) service creates a typical supply-and-demand two-sided market, which enables riders and drivers to establish optimized rides conveniently via mobile applications. Group ridesharing is a novel form of ridesharing, which allows a group of riders to share a vehicle that holds the minimum aggregate distance to the whole group. Accompanied by the advantage of ORH services, there comes some vital privacy concerns. In this article, we propose a privacy-preserving online group ridesharing matching scheme for ORH services, called PGRide. PGRide can select the nearest driver to serve a group of riders, without leaking the location privacy of both riders and drivers. In PGRide, we propose an encrypted aggregate distance computation approach by using somewhat homomorphic encryption with ciphertexts packing, which efficiently computes the aggregate distances from a group of riders to large-scale dynamic drivers in encrypted form. Meanwhile, we design a secure minimum selection protocol by using ciphertexts packing and blinding, which efficiently finds the minimum element from a set of encrypted integers without leaking any actual element value. Theoretical analysis and performance evaluations prove that PGRide is secure, accurate, and efficient.

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

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.

Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Private set intersection (PSI) is a fundamental cryptographic primitive, allowing two parties to calculate the intersection of their data sets without exposing additional private information. In cloud-based IoT system, IoT-enabled devices would like to outsource their data sets in their encrypted form to the cloud. In this scenario, how to delegate the set intersection computation over outsourced encrypted data sets to the cloud and how to achieve the fine-grained access control for PSI without divulging any additional information to the cloud are still open problems. With that in mind, in this work, we combine key-policy attribute-based encryption (KP-ABE) and PSI to introduce such a novel concept, called delegated key-policy attribute-based set intersection over outsourced encrypted data sets (KP-ABSI), to solve this problem. Then we propose a first concrete KP-ABSI scheme and analyze its efficiency.

A HOMOMORPHIC ENCRYPTION BASED ON-LINE VOTING SYSTEM

This paper developed for the risk free and person oriented Online Voting System. The Online Voting gadget is made for the humans of the united states living round the world and needs to vote for their representative. The election can be carried out in two methods the paper ballot election and the computerized ballot elections. The automatic ballot elections are referred to as the digital voting. The on line vote casting device is rather developed and the on-line polling machine can be changed through precisely and at once vote casting on line and on the spot results. The on line balloting gadget is completed by using the net so it can be known as the Internet Voting. The gadget proceeds the on-line vote casting machine in a new approach known as Homomorphic Encryption . Homogeneous encryption is the form of encryption, which lets the computer generate encrypted end results in ciphertexts, as though they were performed in a plaintext, when decrypted, which matches the end result of the operations. In this paper we have a digital voting device based on homomorphic encryption to make sure that the vote is confidential. The benefits of multi-homorphic encryption systems are all presented in our suggestion. The proposed electoral system is suitable for elections that include non-partial votes and for multi-candidate elections. For outsourced storage and counting, homomorphic encryption can be used.