Encrypted Form Research Articles

A Literature Survey on Privacy Preserving in Image processing on Cloud

In the last decade cybercrime shows tremendous growth. There are different platforms available, in which cyber-attacks are performed, data servers one of the most laid-back targets. Due to these attacks users important information leaked such as personal documents and other private documents. Due to this problem researchers are focused on secure and privacy preserved image transmission system, in which images are send to cloud server in encrypted form and avoid the privacy linking problems in cloud services. There are different types of services provide by cloud IaaS, PaaS and,SaaS all required a trustful and secure method for the encryption of users data. In this survey paper discuss the different privacy preserving methods in Image Cloud, also compare these methods. In the last decade different methods was presented, in this survey discuss few of them. Most of the methods are focused on data security; few of them focus on privacy preservation. Also discuss the major issues of privacy preserving in clouds.

Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management

Small and medium-sized businesses (SMEs) in developing economies still face a number of obstacles that prevent them from adopting digital technologies. In contrast, SMEs have achieved greater success in emerging markets. Due to its potential benefits for numerous businesses, machine learning (ML) has become a hot topic in recent years. Particularly a few major organizations, for example, Amazon, Google and Microsoft have shown a few effective cases on coordinating simulated intelligence capacity in their own organizations. This research suggests a fresh method for bettering private companies by combining large-scale data analysis with artificial intelligence and enhanced safety measures. Here, cloud edge administration with task planning using a dynamic joined real channel Kubernetes obstruction task scheduler improves business for the executives. Then, the organization's security is bolstered by a form of differential encryption on the blockchain that takes into account the need for security. We also propose assigning new jobs to the load node with the lightest workload. Experiments show that our strategy shortens job completion times and distributes work evenly across edge nodes. The experimental investigation is conducted in terms of latency, quality of service, energy efficiency, data integrity, and scalability. The proposed technique attained latency of 0.8354, QoS of 0.9395, energy efficiency of 0.9879, data integrity of 0.1189, scalability of 0.8400.

CrowdFL: Privacy-Preserving Mobile Crowdsensing System Via Federated Learning

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good scalability and low deployment cost but raises privacy concerns. In this paper, we propose a privacy-preserving MCS system called \textsc{CrowdFL} by seamlessly integrating federated learning (FL) into MCS. At a high level, in order to protect participants' privacy and fully explore participants' computing power, participants in \textsc{CrowdFL} locally process sensing data via FL paradigm and only upload encrypted training models to the server. To this end, we design a secure aggregation algorithm (\texttt{SecAgg}) through the threshold Paillier cryptosystem to aggregate training models in an encrypted form. Also, to stimulate participation, we present a hybrid incentive mechanism combining the reverse Vickrey auction and posted pricing mechanism, which is proved to be truthful and fail. Results of theoretical analysis and experimental evaluation on a practical MCS scenario (human activity recognition) show that \textsc{CrowdFL} is effective in protecting participants' privacy and is efficient in operations. In contrast to existing solutions, \textsc{CrowdFL} is 3 <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\times$</tex></formula> faster in model decryption and improves an order of magnitude in model aggregation.

A blockchain-based privacy-preserving auditable authentication scheme with hierarchical access control for mobile cloud computing

Blockchain-based authentication mode, a fundamental solution to prevent unauthorized access behavior, gradually becomes a focus in future distributed mobile cloud computing (MCC) services. However, due to the transparent and immutable characteristics of blockchain, users’ access behaviors are facing huge security and privacy threats. Storing the encrypted data on chain is an effective way to address these issues, but access permission confirmation and update in the form of ciphertext is the main bottleneck. To this end, this paper proposes a blockchain-based unified authentication and hierarchical access control scheme for the MCC environment, which provides both privacy protection and auditability. In the proposed scheme, users can access multiple MCC services with different access permissions using a single credential. To protect the privacy of both users and service providers, while still supporting auditability, the data on the public ledger is blinded using Pedersen commitments. Besides, the proposed scheme provides flexible dynamic updating in encrypted form. Theoretical analysis indicates that the proposed scheme can meet various security and privacy requirements in the MCC environment. Compared with related schemes, it has better communication efficiency. Therefore, the proposed scheme is more suitable for the actual MCC environment.

Data Security of Mobile Cloud Computing on Cloud Server

Mobile cloud computing refers to the technology that allows services, such as software, virtual hardware, and bandwidth, to be delivered over the Internet. This technology primarily benefits mobile devices, particularly smartphones. The popularity of mobile cloud computing is rapidly increasing among consumers, and major companies like Apple, Google, Facebook, and Amazon have a large user base in this field. With the help of cloud storage services, users can conveniently access their data anytime, anywhere, and from any device, including mobile devices. While this provides flexibility and scalability in data management, it also introduces new security risks that need to be addressed. However, these security concerns can be mitigated by implementing appropriate data handling practices. Cloud server providers can enhance data security by employing encryption and decryption techniques when storing data in the cloud. In this study, we propose various encryption and decryption methods to safeguard data in the cloud, ensuring that confidential information remains inaccessible to unauthorized individuals or machines due to its encrypted form.

A Cluster-Based Energy-Efficient Secure Optimal Path-Routing Protocol for Wireless Body-Area Sensor Networks.

Recently, research into Wireless Body-Area Sensor Networks (WBASN) or Wireless Body-Area Networks (WBAN) has gained much importance in medical applications, and now plays a significant role in patient monitoring. Among the various operations, routing is still recognized as a resource-intensive activity. As a result, designing an energy-efficient routing system for WBAN is critical. The existing routing algorithms focus more on energy efficiency than security. However, security attacks will lead to more energy consumption, which will reduce overall network performance. To handle the issues of reliability, energy efficiency, and security in WBAN, a new cluster-based secure routing protocol called the Secure Optimal Path-Routing (SOPR) protocol has been proposed in this paper. This proposed algorithm provides security by identifying and avoiding black-hole attacks on one side, and by sending data packets in encrypted form on the other side to strengthen communication security in WBANs. The main advantages of implementing the proposed protocol include improved overall network performance by increasing the packet-delivery ratio and reducing attack-detection overheads, detection time, energy consumption, and delay.

Zero Knowledge Proof for SNAP (Standar Nasional OPEN API Pembayaran) in Indonesia

SNAP (Standar Nasional OPEN API Pembayaran) is an implementation of open banking for encouraging digital transformation in the banking industry. SNAP was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is already a customer data protection mechanism between the bank, the owner of Open API, and the user of Open API. However, there is no data protection process carried out by consumers so third parties, that use the Open API of the bank, do not need to know the customer's data. Based on the web3 protocol, users can store data and transmit only in encrypted form which can only be opened by calculating the data with a pre-agreed smart contract. Banks can work like a decentralized network on web3, where the process of calculating proof and witness is carried out by the bank. Proof and witness are calculated using a zero-knowledge proof protocol, making it difficult to duplicate. For this reason, we propose a new architecture using smart contracts between banks and customers using the ZK-SNARK method. Therefore, there is no significant performance difference between using ZK-SNARK and without ZK-SNARK in the API call process.

Virtual assets of the distributed register

This publication focuses on the definition of “virtual assets of a distributed registry” as an economic category. Analysis of the term showed a lack of unity in the definition. The opportunities provided by the deployment of virtual assets in a distributed registry are new but potentially effective. Virtual assets can be distributed or unallocated registry assets, secured or unsecured. Among the virtual assets of the distributed ledger: tokenized assets and cryptocurrencies. Accordingly, these are types of virtual assets that exist exclusively in the form of a record with an information identifier. But in the case of a tokenized asset, this information is derived from the original asset. And in the case of a crypto asset, this information is not derived from the primary asset. The stakeholders of the virtual assets market are individuals, miners, mining pools, which carry out the storage, exchange, transfer, provision of intermediary services. Disadvantages of the introduction of virtual assets of the distributed register are the possibility of anonymous transfers, complicated process of identification of stakeholders in the virtual assets market, the possibility of financial speculation, money laundering, difficulties with taxation. The advantages include relatively low transaction fees, their irreversibility and rapid validation. Any changes to the data in the chain of blocks are possible only when stakeholders confirm the legitimacy of transactions in accordance with general rules and protocols. This technology prevents data loss because all network members keep an up-to-date, up-to-date copy in encrypted form. The use of blockchain eliminates the possibility of making changes to the data, so the database is considered relevant at the time of access. Data is stored on different servers, data cannot be processed with expired dates, changes are made in real time and it is impossible to forge them. It is noted that the introduction of virtual assets of the distributed register in order to stimulate economic growth requires a joint effort of the science sector, education sector, IT sector, financial sector, the sector of state regulators. The possibilities of distribution register technologies are constantly growing and opening new perspectives.

Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

Comparative analysis of enhanced hybrid encryption algorithm with single encryption algorithm

To accomplish end users data security, it appears that the methods currently used to ensure data security, privacy, integrity, and availability to cloud users are insufficient. An encryption method will be required to guarantee the private preservation of users data while it is in transit or persistent state. The security of cloud data is ensured by a hybrid encryption algorithm that includes the use of the symmetric Advanced Encryption Standard (AES) and an asymmetric RSA algorithm ,combining an asymmetric algorithm (public-key cryptography, or PKC) with a symmetric algorithm (also called private key cryptography), which employs a single key for encryption and decryption, to increase the security of cloud data. Hybird encryption has significant benefits over single encryption in terms of security, speed and computation time. By first encrypting data with AES and then encrypting the AES key with RSA while in the cloud, the suggested method offers a hybrid encryption scheme that improves the security of cloud data. This paper examines various forms of cryptographic encryption and also offers a enchanced hybrid encryption technique for online applications.

Public key authenticated searchable encryption against frequency analysis attacks

To retrieve data in form of encryption, Boneh et al. proposed a primitive called the public key encryption with keyword search (PEKS). Unfortunately, PEKS is inherently vulnerable to the keyword guessing attack (KGA). To resist such an attack, Huang et al. introduced an authenticated version of PEKS (i.e., PAEKS), in which the sender not only encrypts the keyword, but also uses his/her secret key to authenticate it. Since then, many PAEKS schemes have emerged to improve the security model of the PAEKS system.However, none of these PAEKS schemes consider frequency analysis attacks (FAA). This attack works by tabulating the frequency distribution of trapdoors generated by user when searching, i.e., placing trapdoors embedded with the same keyword in the same category and recording the number of trapdoors in each category. These frequencies are then compared with the priori knowledge of the frequency of keyword searched by user to extract the keyword embedded in the trapdoor, thereby corrupting the user's search privacy.This work formally analyzes how frequency analysis attacks lead to the leakage of keyword information of the trapdoor in the PAEKS system. Then, to resist against this attack, we propose the first PAEKS scheme with frequency analysis attack resistance to protect the user's search privacy. In order to simplify the management of the sender/receiver's public keys, we further propose our second PAEKS scheme based on the first scheme. Finally, we show that both of these two schemes are proven secure under static assumptions.

A Multi-Key with Partially Homomorphic Encryption Scheme for Low-End Devices Ensuring Data Integrity

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version.

Digital Voting System Based on Blockchain Technology

A couple of sorts of casting a ballot have existed from that point. Paper polling forms are the most often used kind of casting a ballot from one side of the planet to the other. An electronic democratic methodology has lately turned out to be notable, yet unsolvable. Security, credibility, straightforwardness, fixed quality, and helpfulness are among the issues that e-casting a ballot strategy raise. In any case, there are a couple of blockchain-based different choices. Blockchain might conceivably tackle every one of the difficulties outlined above while similarly giving benefits like consistent nature and decentralization. The fundamental load of blockchain-based e-voting progress is their restricted highlight on a singular field or the other hand a shortfall of testing and assessment. We portray a blockchain-based e-voting stage in this article that may be used for digital voting. Blockchain utilizes it, and all cycles may be done inside it. After the democratic begins, the stage was limited to a free what's more, decentralized stage with no ability to affect the digital voting framework. The data is absolutely direct, however similar form of encryption is used to protect voter's characters. In three specific blockchains, we attempted and considered our response. The disclosures suggest that public and private blockchains may be used with minor speed contracts

CADF-CSE: Chaotic map-based authenticated data access/sharing framework for IoT-enabled cloud storage environment

Data is an essential asset of an organization or individual in this information age. Secure and resource-efficient data communication has become paramount in the IoT-enabled cloud storage environment. The users must communicate with the cloud storage servers to access, store, and share the data utilizing the public communication channel, which is exposed to various security threats. Moreover, various security frameworks have been presented to render secure data access, storage, and sharing functionalities for the cloud storage environment. Most of them are complicated and incapacitated of resisting various security attacks. Thus, it is imperative to design a secure and resource-efficient data access, storage, and sharing framework for the cloud storage environment. This paper presents a chaotic map-based authenticated data access/sharing framework for the IoT-enabled cloud storage environment (CADF-CSE). CADF-CSE is designed using the chaotic map, authenticated encryption scheme (AEGIS), and one-way hash function (Esch256). The proposed CADF-CSE comprises three significant phases user access control, data storage, and data sharing. The user access control phase enables the user and cloud server to attain mutual authentication followed by the secret session key establishment. Using the established SK during the access control phase user and cloud server exchange information securely across the public Internet. The data storage phase facilitates the data owner to store the data on a cloud server in encrypted form, where encryption is performed with a secret key derived from the user’s biometric. The data-sharing phase enables users to access the data from the cloud server after acquiring mutual permission from the cloud server and the data owner. In addition, an explication of the CADF-CSE through formal and informal analysis shows its resilience to various security attacks. Finally, the performance comparison explicates that CADF-CSE renders better security features while requiring lower computational and communication costs than the related security frameworks.

Homomorphic Encryption and its Application to Blockchain

The concept, method, algorithm and application of the advanced field of cryptography, homomorphic encryption, as well as its application to the field of blockchain are discussed in this paper. There are various forms of homomorphic encryption and they can be classified into three categories: partially homomorphic encryption, hierarchical homomorphic encryption and fully homomorphic encryption. We explain the concept, method and algorithm for each category with down to earth examples. The milestone example of RSA algorithm is explained which is still widely used in the internet and in blockchain as well. Applications of homomorphic encryption depends on efficiency of implementation which has been in good progress in recent years.

CADEN: Compression-Assisted Adaptive Encoding to Improve Lifetime of Encrypted Nonvolatile Main Memories

Nonvolatile memories are widely considered as a potential replacement of DRAM in main memory due to their high density and low-leakage power consumption. The data in these memories are stored in encrypted form to protect them from data stealing. However, the encryption techniques, on account of their diffusion property impose high randomization in the generated encrypted data. It leads to enormous bit-flips in the memory cells, leading to their early wear out. In this letter, we propose a technique called CADEN that combines our compression technique COMF with an adaptive encoding scheme. The compressed data generated by COMF is encoded in finer granularity using an adaptive encoding approach at low storage overhead. Experimental evaluation shows that CADEN reduces bit-flips and improves lifetime in PCM main memory compared to baseline and existing techniques. In particular, CADEN shows 52% and 57% reduction in bit-flips and energy consumption and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.31\times $ </tex-math></inline-formula> improvement in lifetime over baseline.

Secured and Privacy-Preserving Multi-Authority Access Control System for Cloud-Based Healthcare Data Sharing

With continuous advancements in Internet technology and the increased use of cryptographic techniques, the cloud has become the obvious choice for data sharing. Generally, the data are outsourced to cloud storage servers in encrypted form. Access control methods can be used on encrypted outsourced data to facilitate and regulate access. Multi-authority attribute-based encryption is a propitious technique to control who can access encrypted data in inter-domain applications such as sharing data between organizations, sharing data in healthcare, etc. The data owner may require the flexibility to share the data with known and unknown users. The known or closed-domain users may be internal employees of the organization, and unknown or open-domain users may be outside agencies, third-party users, etc. In the case of closed-domain users, the data owner becomes the key issuing authority, and in the case of open-domain users, various established attribute authorities perform the task of key issuance. Privacy preservation is also a crucial requirement in cloud-based data-sharing systems. This work proposes the SP-MAACS scheme, a secure and privacy-preserving multi-authority access control system for cloud-based healthcare data sharing. Both open and closed domain users are considered, and policy privacy is ensured by only disclosing the names of policy attributes. The values of the attributes are kept hidden. Characteristic comparison with similar existing schemes shows that our scheme simultaneously provides features such as multi-authority setting, expressive and flexible access policy structure, privacy preservation, and scalability. The performance analysis carried out by us shows that the decryption cost is reasonable enough. Furthermore, the scheme is demonstrated to be adaptively secure under the standard model.

Smart streetlight system using mobile applications: secured fault detection and diagnosis with optimal powers

A street lighting system is a very essential part of the highways and streets of a smart city. Managing power consumption and maintenance of a street light system will be a challenging task in huge countries. The proposed work is mainly focused on the minimization of power consumption in the implementation of a smart street lighting system. Also, use a mobile application for setting up the brightness levels of the lamps in an encrypted form so that an unauthorized person will not be able to modify the settings. In the existing streetlight system, wireless sensors are installed to control and monitor the streetlamps. In the proposed system, using an nRF24L01 radio transceiver module, a secured communication link is established to operate the streetlights depending on the ambient weather conditions, movement of humans, vehicles and any other objects. A failsafe mechanism is implemented in the modules for conventional lamp operation in the case of module failures. Light-dependent resistor (LDR) is used to determine the ambient brightness levels to automatically turn on/off the streetlights based on weather conditions and lighting on roads. Using smartphones, we access and control the brightness information from the master node at which the nRF24L01 radio transceiver module is installed, and the same information is relayed to all the slave nodes. The results show that we could effectively monitor and control the brightness of streetlights in a secure way and there is a significant amount of power savings. The proposed system saves the average powers of 53.45%, 44.76%, 39.39%, and 32.25% respectively for 10%, 20%, 30%, and 50% idle mode brightness compared to the state-of-the-art techniques present in the literature.

Chorioangioma: a single tertiary care center retrospective study.

Chorioangioma represents a challenge due to the rarity of the condition, paucity of sufficient management guidelines, and controversies regarding the best invasive fetal therapy option; most of the scientific evidence for clinical treatment has been limited to case reports. The aim of this retrospective study was to review the natural antenatal history, maternal and fetal complications, and therapeutic modalities used in pregnancies complicated with placental chorioangioma at a single Center. This retrospective study was conducted at King Faisal Specialist Hospital and Research Center (KFSH&RC) in Riyadh, Saudi Arabia. Our study population included all pregnancies with ultrasound features of chorioangioma, or histologically confirmed chorioangiomas, between January 2010 and December 2019. Data were collected from the patients' medical records, including the ultrasound reports and histopathology results. All subjects were kept anonymous; case numbers were used as identifiers. Data collected by the investigators were entered into Excel worksheets in an encrypted format. A MEDLINE database was used to retrieve 32 articles for literature review. Over a 10-year period between January 2010 and December 2019, 11 cases of chorioangioma were identified. Ultrasound remains the gold standard for diagnosis and follow-up of the pregnancy. Seven of the 11 cases were detected by ultrasound, allowing proper fetal surveillance and antenatal follow-up. Of the remaining six patients, oneunderwent radiofrequency ablation, two underwent intrauterine transfusion for fetal anemia due to placenta chorioangioma, one had vascular embolization with an adhesive material, and two were managed conservatively until term with ultrasound surveillance. Ultrasound remains the gold standard modality for prenatal diagnosis and follow-up of pregnancies with suspected chorioangiomas. Tumor size and vascularity play a significant role in the development ofmaternal-fetal complications and the success of fetal interventions. To determine the superior modality of fetal intervention mandates more data and research; nevertheless, Fetoscopic Laser Photocoagulation and embolization with adhesive material seem to be a lead choice, with reasonable fetal survival.

A Survey on Consensus Protocols and Attacks on Blockchain Technology

In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.