Decryption Time Research Articles

Two Fish Encryption Based Blockchain Technology for Secured Data Storage

Data security and sharing remains nuisance among many applications like business data, medical data, banking data etc. In this research, block chain technology is built with encryption algorithm for high level data security in cloud storage. Medical data security seems critical aspect due to sensitivity of patient’s information. Unauthorized access of medical data creates major issue to patients. This article proposed block chain with hybrid encryption technique for securing medical data stored in block chain model at cloud storage. New Two fish encryption model is implemented based on RSA Multiple Precision Arithmetic (MPA). MPA works by using library concept. The objective of using this methodology is to enhance security performance with less execution time. Patient data is processed by encryption algorithm and stored at blockchain infrastructure using encrypted key. Access permission allows user to read or write the medical data attached in block chain framework. The performance of traditional cryptographic techniques is very less in providing security infrastructure. Proposed blockchain based Two fish encryption technique provides high security in less encryption and decryption time.

Hidden Real Modulus RSA Cryptosystem

Cryptographic techniques in cyber security can be categorized into symmetric and asymmetric. Among asymmetric cryptographic techniques, the RSA algorithm is more popular and considered as secured. Since, RSA uses the common modulus in both encryption and decryption, this modulus is openly available for the public which makes it exposed for attack. Its security is based on the assumption of large integer factorization problem, but this could leave it open to different cryptanalysis attacks: low private exponent attack, Shor’s polynomial-time quantum algorithm, quantum inverse Fourier transform and phase estimation. To address these shortcomings, this paper proposes a public-key security algorithm called Hidden Real Modulus RSA (HRM-RSA) which hides real modulus by masking it. The public mask modulus which is a pseudo random masking number is derived from real modulus. Then, this derived public mask modulus is introduced in a public key component; as a result, a real modulus is kept hidden from the public unlike the case in RSA. Encryption is done using this public mask modulus and the decryption process is done using a private hidden real modulus. For performance analysis Net bean IDE 8.2 is used, and the proposed algorithm is compared with state-of-the-art algorithms: RSA, ESRKGS, and MRSA based on security strength, time complexity, key generation time, encryption speed, and decryption speed. The performance analysis shows that HRM-RSA is less complex but highly secured than existing algorithms. It improves key generation time of ESRKGS, and MRSA by 311%, 42%; encryption time of RSA, ESRKGS, MRSA by 0.7%, 139%, 735%; decryption time of RSA, ESRKGS, MRSA by 3%, 138%, 799%, respectively.

Blockchain- Based Secure and Efficient Scheme for Medical Data

Internet of Things (IoT) fog nodes are distributed near end-user devices to mitigate the impacts of low delay, position awareness, and spatial spread, which aren't permitted by numerous IoT apps. Fog computing (FC) also speeds up reaction times by decreasing the quantity of data sent to the cloud. Despite these advantages, FC still has a lot of work to do to fulfill security and privacy standards. The constraints of the FC resources are the cause of these difficulties. In reality, FC could raise fresh concerns about privacy and security. Although the Fog security and privacy problems have been covered in several articles recently, most of these studies just touched the surface of these difficulties. This paper provides a unique solution for the authentication of data by using hyperledger fabric. The fog layer store data transferred by the IoT layer and calculate the hash value. These hash values are now stored in hyperledger fabric for authentication purposes. The proposed model results compared with lewako’s and Fan’s scheme and found that the proposed model has 25.00 % less encryption time, 09.3 % less decryption time, 17.48 % less storage overhead, and 23.38 % less computation cost as compared to Fan’s scheme.

A lattice‐based blind ring signature scheme for sensitive data protection in blockchain applications

SummaryEvery transaction is made public and verified by a third party, and the transparency of blockchain applications leads to privacy leaks. Everyone can access the transaction, making it easier for network analysis to identify the user's identity, which is a major problem with the blockchain. Financial institutions have become hesitant to adopt blockchain technology; as a result, preventing its spread. To confirm that encryption and security are maintained for transactions in the blockchain, this research introduces a zero‐knowledge proof (ZKP) based transaction validation scheme. To increase the security of blockchain applications, the proposed lattice‐based blind ring signature (LBRS) scheme integrates blind and ring signatures and is applied to sensitive data. The data are encrypted using this encryption scheme before being stored with the transactional information in the blocks. The encrypted data is then stored in the blocks, which users can retrieve after verification. The encrypted data is then stored in blocks, which users can retrieve after verification. A verification scheme based on the ZKP is used to prevent unauthorized access and changes to the data. The proposed LBRS scheme meets the security analysis requirement. The performance of an LBRS scheme is compared to other relevant models in terms of execution time, encryption time, and decryption time. Overall, the results are more encouraging and reassuring than other relevant works of interest. Finally, this model provides better computational security and satisfies the requirements like correctness and security analysis.

Maximizing blockchain security: Merkle tree hash values generated through advanced vectorized elliptic curve cryptography mechanisms

SummaryCloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively.

Secure MapReduce Framework with Data Compression Model to Evaluate Ideological Evaluation Contribution on Students

Ideological education is the process of transmitting and instilling a particular set of beliefs, values, and worldviews in individuals, with the goal of shaping their attitudes, behaviors, and perceptions. In colleges, ideological education plays an important role in shaping students' values, beliefs, and attitudes toward society, politics, and culture. The primary objective of ideological education in colleges is to promote critical thinking skills, social responsibility, and civic engagement among students. However, the use of big data in ideological education also raises concerns about privacy and ethical considerations. It is important to ensure that students' personal information is kept secure and that data is only used for educational purposes. It is also important to ensure that the use of big data does not lead to bias or discrimination of certain students or groups based on their demographics or backgrounds. This paper constructed a framework of Stream Cipher MapReduce Fractal Index (ScMFI). The ScMFI model performs the processing of information about students in ideological education in colleges. Through the Fractal Index Tree data is compressed with the MapReduce Framework. Finally, the ScMFI model uses the Stream Cipher process for data processing and storing in the cloud environment. The developed ScMFI model contribution of ideological education toward students’ awareness is classified as a machine learning model. The simulation analysis stated that ScMFI model minimizes the data storage, encryption and decryption time.

BLOCKCHAIN-BASED ZERO KNOWLEDGE PROOF MODEL FOR SECURE DATA SHARING SCHEME IN A DISTRIBUTED VEHICULAR NETWORKS

The possibility of implementing advanced applications, such as improved driving safety, has increased with the rapid development of vehicular telematics, and existing vehicular services have been enriched through data sharing and analysis between vehicles. This research uses smart contracts and consortium blockchain zero knowledge proof to secure data sharing and storage in vehicular networks. The results indicate that, for message sizes (m), both data_ experiments _2 and 1 produce ciphertext of the same size 157 bits, with the exception of 'gnfuv-temp-exp1-55d487b85b-5g2xh,' which generates ciphertext of 156 bits with the lowest decryption time of 26,865ms and a small decrease in encryption time between 28,620ms and 28,162ms. the proposed model validation shows that the model performed better than the Advanced encryption standard in terms of ciphertext size, encryption time and decryption time in comparison and it satisfies the good and robust blockchain-based zero knowledge proof model for secure data sharing and storage for distributed VANET. The scheme achieves high levels of security while operating with reasonable efficiency, reliability and availability according to numerical results.

Performance Analysis of Two Famous Cryptographic Algorithms on Mixed Data

The rapid development of digital data sharing has made information security a crucial concern in data communication. The information security system heavily relies on encryption methods. These algorithms employ strategies to increase data secrecy and privacy by obscuring the information, which only those parties who have the accompanying key can decode or decrypt. Nevertheless, these methods also use a lot of computational resources, including battery life, memory, and CPU time. So, to determine the optimal algorithm to utilize moving forward, it is necessary to assess the performance of various cryptographic algorithms. Therefore, this study evaluates two well-known cryptographies (RSA and ElGamal) using mixed data such as binary, text, and image files. CPU internal clock was used to obtain the time complexity used by both algorithms during encryption and decryption. The algorithms used CPU internal memory to obtain memory usage during the encryption and decryption of mixed data. Evaluation criteria such as encryption time, decryption time, and throughput were used to compare these encryption algorithms. The response time, confidentiality, bandwidth, and integrity are all factors in the cryptography approach. The results revealed that RSA is a time-efficient and resourceful model, while the ElGamal algorithm is a memory-efficient and resourceful model.

Perbandingan algoritma kriptografi simon dan vigenere dalam mengamankan citra digital

Digital image files or images are sometimes a valuable asset. Digital images that are private and confidential are very vulnerable to interception by other parties, especially if the image is distributed via the internet. To increase the security of digital images so that their confidentiality can be maintained, a special technique is needed to protect digital image messages, namely with cryptographic techniques. This study aims to determine the performance of the SIMON algorithm for digital image security. SIMON algorithm performance results are compared with the vigenere cipher algorithm in terms of time and image file size produced. In this study used base64 encode for the encryption process and base64 decode for the decryption process. The performance of the SIMON algorithm in securing digital images results in an average encryption time of 969 ms and an average decryption time of 1537 ms. The SIMON algorithm requires a longer time for the encryption and decryption process when compared to the Vigenere algorithm. The cipher image encrypted by the SIMON algorithm has a size larger than the original file by 36%. However, when compared to the cipher image encrypted by the Vigenere algorithm, there is no significant difference. The UACI value obtained from the SIMON algorithm cipher image obtained an average yield of 18.94%. Based on the theory of differential analysis, it can be said that this value is still vulnerable to differential attack. This is based on the UACI value which has not met the minimum threshold value of 33%.

Proposed Hybrid Cryptosystems Based on Modifications of Playfair Cipher and RSA Cryptosystem

Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared RSA to get the final cipher text. This algorithm achieved higher security to data but suffers from a long computational time. So Chinese remainder theorem has been used in the second hybrid cryptosystem to obtain less encryption and decryption time. The simulation results indicated that using the modified Playfair with the proposed square RSA has improved security. Moreover, using the Chinese remainder theorem achieved less encryption and decryption time in comparison to our first proposed and the standard algorithms.

A Chunked and Disordered Data Privacy Protection Algorithm: Application to Resource Platform Systems

This paper provides a systematic analysis of existing resource platforms, evaluating their advantages and drawbacks with respect to data privacy protection. To address the privacy and security risks associated with resource platform data, we propose a novel privacy protection algorithm based on chunking disorder. Our algorithm exchanges data within a specific range of chunk size for the position and combines the chunked data with the MD5 value in a differential way, thus ensuring data privacy. To ensure the security of the algorithm, we also discuss the importance of preventing client and server decompilation during its implementation. The findings of our experiments are as follows. Our proposed privacy-preserving algorithm is extremely secure and easy to implement. Our algorithm has a significant avalanche effect, maintaining values of 0.61–0.85, with information entropy being maintained at 4.5–4.9. This indicates that our algorithm is highly efficient without compromising data security. Furthermore, our algorithm has strong encryption and decryption time stability. The key length can be up to 594 bits, rendering it challenging to decrypt. Compared with the traditional DES algorithm, our algorithm has better security under the same conditions and approaches the levels of security offered by the AES and RC4 algorithms.

Enhancing Organizational Data Security on Employee-Connected Devices Using BYOD Policy

To address a business need, most organizations allow employees to use their own devices to enhance productivity and job satisfaction. For this purpose, the Bring Your Own Device (BYOD) policy provides controllable access for employees to organize data through their personal devices. Although the BYOD practice implies plenty of advantages, this also opens the door to a variety of security risks. This study investigates these security risks and proposes a complementary encryption approach with a digital signature that uses symmetric and asymmetric algorithms, depending on the organization’s digital certificate, to secure sensitive information stored in employees’ devices within the framework of BYOD policies. The method uses Advanced Encryption System (AES), Blowfish, RSA and ElGamal with a digital signature to achieve strong encryption and address critical security considerations such as user authentication, confidentiality and data integrity. The proposed encryption approach offers a robust and effective cryptographic solution for securing sensitive information in organizational settings that involve BYOD policies. The study includes experimental results demonstrating the proposed approach’s efficiency and performance, with reasonable encryption and decryption times for different key and file sizes. The results of the study revealed that AES and Blowfish have the best execution time. AES has a good balance of security and performance. RSA performs better than ElGamal in encryption and signature verification, while RSA is slower than ElGamal in decryption. The study also provides a comparative analysis with previous studies of the four encryption algorithms, highlighting the strengths and weaknesses of each approach.

Flexible and secure continues data transmission among multiple users in cloud environment

Cloud computing is an internet based computing where the sharable information, software and resources are provided based on demand devices. Where, the rapid development and pervasive growth of unavoidable sending of message advances, there are expanding requests of adaptable cryptographic natives to protected group data transactions and computing platforms in cloud. Group key agreement (GKA) protocol enables a group to share a standard encryption key across an open network so that only members of the group may decode the ciphertexts encoded using the secret encryption key that has been released. However, a sender cannot deny any specific member from decryptions the ciphertexts in cloud. However, before sending a message to a group, a user must join the group and follow the GKA protocol to provide the intended members access to a secret key. To find a better solution for the above-mentioned issues, flexible and secure continues data transmission (FSCDT) algorithm is proposed to offer dynamic and secure data transfer broadcasting without full trust of key authority in unreliable cloud environment. It provides compete security proof, outlines the requirements of the aggregatability of the secret attribute based FSCDT building block. Based on experimental evaluations, FSCDT algorithm minimizes encryption time, decryption time and communication cost.

A Secure and Energy-efficient Framework for Air Quality Prediction Using Smart Sensors and ISHO-DCNN

Background: The World Health Organization (WHO) reported that Air pollution (AP) is prone to the highest environmental risk and has caused numerous deaths. Polluted air has many constituents where Particulate Matter (PM) is majorly reported as a global concern. Currently, the most crucial challenges faced by the globe are the identification and treatment of augmenting AP. The air pollution level was indicated by the Air Quality Index (AQI). It is affected by the concentrations of several pollutants in the air. Many pollutants in the air are harmful to human health. Thus, an efficient prediction system is required. Many security problems and lower classification accuracy are faced by them even though several prediction systems have been formed. A secure air quality prediction system (AQPS) centered upon the energy efficiency of smart sensing is proposed in this paper to overcome these issues. From disparate sensor nodes, the input data is initially amassed in the proposed work. The gathered data is stored in the temporary server. Next, the air-polluted data of the temporary server is offered to the AQPS, wherein preprocessing of the input data along with classification is executed. Methods: Utilizing the Improved Spotted Hyena Optimization-based Deep Convolution Neural Network (ISHO-DCNN) algorithm, the classification is executed. Utilizing the Repetitive Data Coding Based Huffman Encoding (RDC-HE) method, the polluted data attained from the classified output is compressed and encrypted by employing the American Standard Code for Information Interchange based Elliptical Curve Cryptography (ASCII-ECC) method. Results: Afterward, the encrypted and compressed data is saved in the Cloud Server (CS). Finally, for notifying about the AP, the decrypted and decompressed data is offered to the Base Stations (BS). Conclusion: The proposed work is more effective when analogized to the prevailing methods as denoted by the experimental outcomes. Higher accuracy of 97.14% and precision of 91.44% were obtained by the proposed model. Further, lower Encryption Time (ET) and Decryption Time (DT) of 0.565584 sec and 0.005137 sec were obtained by the model.

Proposed Method for Efficient Block Cipher Cryptography

After the use of the Internet and the transmission of files between users expanded, information security became one of the important priorities for securing information by different means and methods. The information encryption comes at the forefront of the techniques used in information security. Encryption has been known since ancient ages. So, traditional methods appeared that quickly and greatly developed. They are still used in many scientific studies and developmental research with the aim of developing new methods for encryption of substitutions and increasing its ability to confront various attacks. The traditional methods were subjected to several attacks that they prompted researchers to develop them and overcome their weaknesses in front of those attacks. Some combinations of these methods are used to design new encryption methods based on overlapping ideas of traditional encryption. This paper sought to use a set of traditional encryption methods in designing a new system. In which, these methods are mixed with modern ideas that help increase the strength of encryption and its ability to face potential attacks. Caesar, Vigenère, Rail Fence, and transpose ciphers, with the idea of cipher cycles and segments and division into two parts in block ciphers. The proposed system relied on the chaotic function Tent map for its random properties in generating a string of keys consisting of 112 random characters in length using Python 3.11 programming language. The text was cut into Blocks of 108 characters. Each Block was divided into two parts. A part of the key was added to each section. A rial fence method was applied. Then Vigenère method with the key is applied. The proposed system processed the original text within the framework of the encryption cycles with the use of substitution in the order of the columns, shifting to the right, and the replacement of the sequence of the elements of each side according to the order of the elements of the other side. After the end of the encrypting cycles, the cipher block is added to the final ciphertext. The operations were repeated for the next block until the last block is finished. For the purpose of decrypting, the previous operations are repeated in reverse order to obtain the original text, by decrypting blocks of the cipher text. The proposed system achieved extreme accuracy in extracting the original text. The system was evaluated by measuring the time and throughput of the encryption and decryption. The encryption and decryption times were short for short texts, which increased with the increase in the length of the text, with a relatively stable encryption and decryption throughput. The system achieved acceptable memory consumption in short texts but increased by the length of the text size. The achieved Avalanche test was less than (50%).

Implementation and evaluation of EMAES – A hybrid encryption algorithm for sharing multimedia files with more security and speed

In this era of smartphones, a huge amount of multimedia files like audio, video, images, animation, and plain text are shared. And with this comes the threat of data being stolen and misused. Most people don’t think about the security of data before uploading it to any platform. Most apps used on smartphones upload our data to their server. Not only this, but other third-party apps can also read that data while it is being transmitted. One solution to this problem is encrypting the data before sharing it and decrypting it back at the other end so that even if it is intercepted in between the transmission, it would be impossible to decrypt it. In this paper, a newly designed hybrid encryption algorithm EMAES that includes the efficiency of MAES (Modified Advanced Encryption Standard) and security of ECC (Elliptic Curve Cryptography) was implemented in MATLAB as well as in android studio 4.0. using a mobile messaging application. Also, it was tested for different speeds and security parameters. Further, it was compared with standard algorithms like the RC4, RC6 and Blowfish as well as with other hybrid algorithms like RC4+ECC, RC6+ECC and Blowfish+ECC. The EMAES was found 30% more efficient in terms of encryption and decryption time. The security of EMAES also showed improvement when compared with other hybrid algorithms for parameters like SSIM (structural similarity index measure), SNR (Signal to Noise Ratio), PSNR(Peak Signal to Noise Ratio), MSE (Mean Squared Error) and RMSE (Root Mean Squared Error). And finally, no significant improvement was found in the CPU and RAM usage.

New attribute‐based encryption schemes with anonymous authentication and time limitation in fog computing

SummaryFog computing is a suitable platform for the Internet of Things (IoT). However, it faces threats that pose security and privacy challenges. This article proposes two new, faster, and more secure schemes based on attribute‐based encryption (ABE) address some security concerns. The new schemes are Cipher text‐policy‐ABE‐anonymous authentication (CP‐ABE‐AA) and key‐policy‐ABE‐anonymous authentication (KP‐ABE‐AA), providing more secure communications and anonymous recognition between servers and users employing registration center (RC). Users and servers register with RC and receive time parameter t and aliases to build an access tree. During the valid time, t a more secure transfer is established. After each t has expired, the servers and users obtain new aliases. RC provides anonymous authentication, and t determines the key's validity period. For simulation, a multi‐paradigm programming language, Rust, was installed on Ubuntu 20. Compared to five existing schemes, the simulation results indicated that, on average, CP‐ABE‐AA has a 5.48% reduction time for key generation but an increased time of 11.28% in encryption and a reduction time of 11.33% in decryption. Also, compared to two other schemes, KP‐ABE‐AA produced an average time increase of 17.31% for key generation, but encryption and decryption times were reduced by 17.31% and 9.57%.

Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing.

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE).

Protecting Digital Images Using Keys Enhanced by 2D Chaotic Logistic Maps

This research paper presents a novel digital color image encryption approach that ensures high-level security while remaining simple and efficient. The proposed method utilizes a composite key r and x of 128-bits to create a small in-dimension private key (a chaotic map), which is then resized to match the color matrix dimension. The proposed method is uncomplicated and can be applied to any image without any modification. Image quality, sensitivity analysis, security analysis, correlation analysis, quality analysis, speed analysis, and attack robustness analysis are conducted to prove the efficiency and security aspects of the proposed method. The speed analysis shows that the proposed method improves the performance of image cryptography by minimizing encryption–decryption time and maximizing the throughput of the process of color cryptography. The results demonstrate that the proposed method provides better throughput than existing methods. Overall, this research paper provides a new approach to digital color image encryption that is highly secure, efficient, and applicable to various images.

Protecting Information Stored Inside the Cloud with A New CCA-EBO Protocol Designed on Hive Technology

Massively scalable facilities may be accessed online with ease due to "Cloud Computing (CC)". The CC resources are primarily characterized by the fact that "Cloud User (CU)" information is often kept on "Cloud Server (CS)" that the CU doesn't even possess or control. The CUs' apprehension about the loss of management of their information may lead to a substantial roadblock in their acceptance of CC offerings. In an attempt to highlight the effectiveness of CC security, the "Cloud Service Providers (CSP)" need to empower the CU to control and evaluate their data. The focus of this research chooses to highlight a key aspect of CC platforms employed to handle CU information on unrecognized CSs at remote locations. Concerns about compromising personal information arise from this feature's importance. In this research, a novel swarm-based "Enhanced BAT Optimization (EBO)" for key generating in "Cloud Computing Accountability (CCA)" for CC information tracking is proposed to solve security issues. Generally, this proposed hybrid CCA-EBO architecture is based on the idea of data accountability, which enables dispersed end-to-end responsibility. The information is made available to the general public, although with a limited set of permissions. The "Cloud Administrator (CA)" would specify the level of access each CU has to the data before it is made available to them. All CU accesses to data are recorded and will be found in a log file for CA to review. According to evaluation methods for the proposed CCA-EBO, existing "Hybrid Secure Cloud Storage (HSCS)", and "Advanced Distribution Verification Protocol (ADVP)", the CCA-EBO provides more security than HSCS, and ADVP in terms of "Auditing Time", "Encryption Time", "Decryption Time", and "Storage Overhead".