Data Encryption Standard Research Articles

Selective Encryption Algorithm for Vectoral Geographic Data under Feature Point Grouping Strategy

Abstract. Selective encryption technology is a data security protection method that can balance encryption security and efficiency. Currently, there is ongoing research on applying this technology to vector geographic data. However, existing algorithms often use random selection methods for choosing objects to be encrypted, which results in lower security. To address this problem, we proposed a selective encryption algorithm for vector geographic data based on feature point grouping strategy. The study includes four steps: (1) Based on the user-input encryption ratio, calculate the feature point grouping thresholds for each element in the data. (2) Extract feature point sets for each element based on the grouping threshold. (3) Sort and group the feature point sets, reducing the smallest encryption unit to group objects within individual elements. (4) To reduce encryption costs and enhance the algorithm's resistance to attacks, perform frequency domain coefficient encryption and spatial domain coordinate value encryption in a stepwise manner on the group objects, ultimately producing the encryption results. The experiment showed that: (1) The decrypted encrypted data maintains consistency with the original data coordinates, achieving lossless decryption. (2) The correlation between encrypted data and the original data is significantly disrupted, resulting in a high level of randomness and thereby ensuring the algorithm's robust security. (3) The key space is substantial and highly sensitive, capable of withstanding brute force attacks. (4) The algorithm significantly improves encryption efficiency. (5) The algorithm displays resilience against deletion attacks and noise attacks. In conclusions, the proposed method enhances encryption efficiency while upholding a high level of security, thus it is an efficient selective encryption algorithm for vector geographic data.

Hybridized Data Encoding based Encryption and Diffie Hellman Decryption for Security Enhancement

Information security is extremely important because the modification and interception of the data can lead to loss of data integrity, confidentiality, and availability. The encryption process is a critical component of classic standard encryption techniques such as advanced encryption, standard block ciphers, and data encryption standards. The traditional algorithm requires increased power consumption and memory. Therefore, there is a need to develop an efficient cryptographic operation to promote better security. The encryption process is performed using the Hybridized Data Encoding (HDE) model. Here, the optimal keys can be selected using the Chaotic Coati optimization algorithm (ChCoA). The decryption process can be performed using the Enhanced Diffie Hellman algorithm (EDHA). The resulting achievements will include effective encryption and decryption algorithms that together strengthen data security, facilitate secure data exchange, and provide superior protection against data vulnerabilities. By implementing the proposed work in the Matlab working platform, the performances in terms of encryption time, decryption time, processing time and throughput are analyzed. The proposed model achieved a throughput of 249.1093 (kb/s) and a processing time of 0.55 (sec).

General secure encryption algorithm for separable reversible data hiding in encrypted domain

The separable reversible data hiding in encrypted domain (RDH-ED) algorithm leaves out the embedding space for the information before or after encryption and makes the operation of extracting the information and restoring the image not interfere with each other. The encryption method employed not only affects the embedding space of the information and separability, but is more crucial for ensuring security. However, the commonly used XOR, scram-bling or combination methods fall short in security, especially against known plaintext attack (KPA). Therefore, in or-der to improve the security of RDH-ED and be widely applicable, this paper proposes a high-security RDH-ED encryption algorithm that can be used to reserve space before encryption (RSBE) and free space after encryption (FSAE). During encryption, the image undergoes block XOR, global intra-block bit-plane scrambling (GIBS) and inter-block scrambling sequentially. The GIBS key is created through chaotic mapping transformation. Subsequently, two RDH-ED algorithms based on this encryption are proposed. Experimental results indicate that the algorithm outlined in this paper maintains consistent key communication traffic post key conversion. Additionally, its computational complexity remains at a constant level, satisfying separability criteria, and is suitable for both RSBE and FSAE methods. Simultaneously, while satisfying the security of a single encryption technique, we have expanded the key space to 28Np×Np!×8!Np, enabling resilience against various existing attack methods. Notably, particularly in KPA testing scenarios, the average decryption success rate is a mere 0.0067% and 0.0045%, highlighting its exceptional security. Overall, this virtually unbreakable system significantly enhances image security while preserving an appropriate embedding capacity.

Innovative Soft Cryptosystem for Encrypting and Decrypting Messages with IDEA

This paper introduces an advanced cryptosystem for message encryption and decryption, integrating the International Data Encryption Algorithm (IDEA) with soft set and soft matrix principles. Soft sets, conceptualized by Molodtsov, are adept at managing uncertainty, making them ideal for cryptographic frameworks. Our approach utilizes inverse and characteristic products of soft sets and matrices, combined with the robust IDEA algorithm, to significantly enhance security. The IDEA algorithm operates on 64 -bit data blocks with a 128 -bit key, ensuring strong encryption. Additionally, we incorporate the symmetric to introduce complex permutations, further strengthening the cryptographic process by ensuring unique encryptions for identical plaintexts. This layered approach drastically improves resilience against unauthorized decryption. Practical applications demonstrate the system's efficacy, highlighting its superior security and reliability for data transmission in environments with high uncertainty. This innovative cryptosystem provides a formidable solution for contemporary encryption challenges.

Analysis of Image Encryption using Triple DES

Abstract: Keeping your essential data secure in today’s world is very essential. There are different types of cyber-attacks which attackers can use to exploit your personal or confidential data for their own needs. Cryptography and its algorithms in cybersecurity has been an important part in providing secure methods to hide or encrypt information or sensitive data which only the person who was intended to be send can access it. This paper presents the triple Data Encryption Standard (DES) approach for secure image encryption from malicious users. This paper provides an efficient, easy and secure approach for encryption of images which is essential in today’s scenario.

A secure data transmission framework for IoT enabled healthcare

The Internet of Medical Things (IoMT) has transformed healthcare by connecting medical devices, sensors, and patients, significantly improving patient care. However, the sensitive data exchanged through IoMT is vulnerable to security attacks, raising serious privacy concerns. Traditional key sharing mechanisms are susceptible to compromise, posing risks to data integrity. This paper proposes a Timestamp-based Secret Key Generation (T-SKG) scheme for resource-constrained devices, generating a secret key at the patient's device and regenerating it at the doctor's device, thus eliminating direct key sharing and minimizing key compromise risks. Simulation results using MATLAB and Java demonstrate the T-SKG scheme's resilience against guessing, birthday, and brute force attacks. Specifically, there is only a 9 % chance of key compromise in a guessing attack if the attacker knows the key sequence pattern, while the scheme remains secure against brute force and birthday attacks within a specified timeframe. The T-SKG scheme is integrated into a healthcare framework to securely transmit health vitals collected using the MySignals sensor kit. For confidentiality, the Data Encryption Standard (DES) with various Cipher Block modes (ECB, CBC, CTR) is employed.

A new encryption algorithm for image data based on two-way chaotic maps and iterative cellular automata

Due to their simplicity of implementation and compliance with the encryption issue, chaotic models are often utilized in picture encryption applications. Despite having many benefits, this approach still has a crucial space issue that makes encryption algorithms based on it susceptible to brute-force assaults. This research’s proposed novel picture encryption technique has a vast key space and great key sensitivity. To achieve this goal, the proposed method combines two-way chaotic maps and reversible cellular automata (RCA). First, this approach uses a two-way chaotic model named spatiotemporal chaos for image confusion. This step includes permuting the image pixels using a chaotic map at the byte level. Then, the RCA model is utilized for image diffusion. In this step, the RCA model iterates over image pixels to modify them at the bit level. The method’s performance in encrypting grayscale images was evaluated using various analysis methods. According to the results, the proposed method is a compelling image encryption algorithm with high robustness against brute-force, statistical, and differential attacks.

Improvement the International Data Encryption Algorithm(IDEA)based on Chaos Theory

Background: The (IDEA), which represents International Data Encryption Algorithm is a widely used cipher algorithm that has raised concerns due to the discovery of numerous weak keys, as highlighted in previous research. These weak keys, specifically those containing consecutive sequences of single digits, have been found to impede the algorithm's evolution and result in a sluggish avalanche effect. To address these vulnerabilities, this research aims to develop an enhanced and secure encryption algorithm by incorporating Chaos Theory principles into IDEA. Materials and methods: The primary objective is to address the weaknesses associated with IDEA's key generation process by leveraging Chaos Theory to generate strong, highly random keys that replace the algorithm's weak keys t. The study also aims to assess the security and cryptographic strength of the enhanced IDEA algorithm through evaluations and comparisons with the original algorithm. Results: The results showed that the proposed key passed 11 tests out of the 15 total tests, while the Traditional key of IDEA passed only 7. This indicates that the proposed method is superior by 4 tests over the Traditional method of generating keys. Also, cipher text 1 with the proposed key passed 10 tests out of the 15 total tests, while with the Traditional key passed only 8 . Conclusion: This indicates the superiority of the proposed method of IDEA in encryption over the traditional method of IDEA.

Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

In the modern world, due to changing lifestyles, more people are infected by deadly diseases, especially heart disorders. An early prognosis is the only possible way of increasing survival rate. One of the key elements of this context is the healthcare digitalization that is carried out through Internet of Things (IoTs) and cloud computing. However, main issue in cloud with IoT is false diagnosis, which leads to cause a major impact on patient’s life. Moreover, data communication through cloud servers can be compromised by attackers due to security issues. Therefore, to overcome these issues, this paper proposes a novel secure e-healthcare system with dual objectives- accurate disease prediction and improved cloud security. A novel diagnosis approach ‘Hybrid Binary Particle Firefly Optimized Extreme Learning Machine classifier (HybBPF-ELM)’ to predict HD is designed, that recognize the subject with HD abnormality from the normal ones. The prediction accuracy and time efficiency of training process are improved by the adoption of a Hybrid Binary Particle Firefly Optimizer (HBPFO) through fine-tuning weight and bias of Extreme Learning Machine (ELM) classifier. Additionally, a new intelligent encryption and decryption framework ‘IEDF’ is introduced for cloud security. It combines four encryption algorithms, including Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest- Shamir-Adleman (RSA), and Modified Blow Fish (MBF) to enhance cryptographic strength and key security. Along with this, an Automatic Sequence Encryption (ASC) for data block encryption is employed to ensure strong security and network performance. The integration of HD prediction module and cloud security framework within e-healthcare system assists healthcare experts in securely storing and transferring data for advanced diagnosis. The results show that our proposed system achieves an overall prediction accuracy of 99.36 % and less processing time for encryption and decryption of 127.55 s and 452.01 s at 2.2 GB file size respectively.

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext.

Systematic Literature Review: Cybersecurity by Utilizing Cryptography Using the Data Encryption Standard (DES) Algorithm

The world of information technology is currently developing very rapidly. This opens up opportunities in the development of computer applications, but it also creates opportunities for threats to alter and steal data or what is often known as cyber-crime. This action is a violation that can cause direct or indirect losses. Therefore, cyber-security is very important to protect user information from cyber-crime. Based on this description, this research will conduct a Systematic Literature Review (SLR) on cyber-security by utilizing cryptography using the DES algorithm. By using the SLR method, literature searches were conducted on Google Scholar or Garuda with the keywords for national journals "Data Encryption Standard Algorithm (DES)" and keywords for international journals "Data Encryption Standard Algorithm (DES)" from both national and international journals, and limiting articles from 2019 to 2023, and obtained selection results as many as 10 articles used from national journals and 10 articles used from international journals. So that this research is expected to increase the understanding of literature that reviews cyber-security by utilizing cryptography using the DES algorithm.

Multi-dimension-precision chaotic encryption mechanism for Internet of Things

The massive amount of information processed in the Internet of Things(IoT) environment requires effective encryption schemes to ensure secure transmission. However, traditional encryption methods such as Elliptic Curve Cryptography (ECC) or Data Encryption Standard (DES) are not suitable for IoT environments where large amounts of data need to be quickly encrypted. In this paper, a multi-precision encryption mechanism based on multi-dimensional chaotic system is proposed. Firstly, the proposed scheme uses floating-point, 32-bit, 24-bit, and 16-bit data accuracy respectively, which can effectively reduce the complexity through fixed-point calculation. Secondly, aiming at the problem that traditional chaotic dynamic analysis methods such as the Lyapunov index cannot be applied, an analysis scheme based on complexity measure is proposed. Finally, a free dimension encryption mechanism based on Logistic, Tent, and PWLCM is proposed. By adding random disturbance to a one-dimensional chaotic map, the weak randomness of the single chaotic sequence is overcome effectively. Finally, the security analysis of the encryption scheme is carried out, and the result proves that the scheme can effectively resist the common attack means. In the 3D chaotic system, the encryption effect of 24-bit data accuracy is close to floating point accuracy, which is suitable for the IoT environment with large data volume, high encryption efficiency, and vulnerability.

A Survey on the Advanced Encryption Standard (AES): A Pillar of Modern Cryptography

As the digital landscape expands and our reliance on secure data transmission grows, the adoption of encryption technologies has become paramount. Over time, we have witnessed a progression from rudimentary ciphering methods, like substitution ciphers, to the sophisticated algorithms of today, epitomized by the Advanced Encryption Standard (AES). AES’s ascendancy to the forefront of encryption can be attributed to its unparalleled security features, surpassing its predecessors such as the Data Encryption Standard (DES). AES boasts robust security measures, rendering it virtually impervious to conventional cryptographic attacks. Its resilience against decryption is underscored by its intricate encryption process, which involves intricate operations such as byte substitution, row shifting, column mixing and round key addition. Conversely, decryption reverses these steps, ensuring the confidentiality of encrypted data remains intact. Despite the emergence of various cryptographic attacks, none have posed a significant threat to a properly implemented Full-AES algorithm. Most attacks target incomplete implementations, highlighting the importance of proper implementation practices in maximizing AES’s security benefits. Beyond its security prowess, AES stands out for its efficiency, sustainability and simplicity. Despite its widespread adoption and robustness against cryptanalytic attacks, AES exhibits an intriguing phenomenon known as error propagation. Through theoretical analysis and empirical investigations, we elucidate the mechanisms by which errors propagate within AES, shedding light on the vulnerabilities they introduce. Additionally, we explore the implications of error propagation in practical scenarios, including its impact on cryptographic protocols, error correction mechanisms and overall system reliability. Our findings underscore the importance of comprehensively understanding and mitigating the effects of error propagation in AES implementations, offering insights into enhancing the resilience of cryptographic systems against unforeseen adversities.

Hazardous Chemicals Logistics Internet of Vehicles Based on Encryption Algorithm

With the advancement of information and communication technology, vehicles role in people's lives is not just for transportation but also equipment for the carrier of mobile communication. The new direction is provided by the Internet for the automobiles development and upgrading. Internet of Vehicles (IoVs) includes smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate for providing the enhanced transportation services such as high traffic efficiency and reduced congestion and accidents. In the hazardous chemical logistics sector, the integration of Internet of Vehicles (IoVs) introduces significant security, privacy, and trust challenges. Vulnerabilities to cyberattacks, such as hacking and data manipulation, threaten the integrity of sensitive information regarding hazardous cargo, while concerns about location privacy and data minimization arise due to the tracking of vehicle movements. Ensuring authentication, authorization, and compliance with regulatory standards is essential for building trust within the IoV ecosystem, as any compromise in supply chain integrity could lead to safety hazards or legal liabilities. Robust encryption algorithms like AES and RSA play a crucial role in securing data transmission, but proper implementation and key management practices are necessary to prevent cryptographic weaknesses. Addressing these issues comprehensively is vital for safeguarding the transportation of hazardous chemicals and ensuring the safety of both the environment and the public. IoVs, suffer from security, privacy and trust issues. In order to solve the problem of hazardous chemical logistics of encryption algorithm, the author proposed a research on the information security of the Internet of Vehicles. Firstly, the information characteristics of dangerous chemicals vehicles are analyzed, then analyze the Data Encryption Standard (DES) algorithm. Advanced Encryption Standard (AES) algorithm is analyzed in the symmetric cryptosystem and then RSA algorithm is analyzed in the public key cryptosystem. Finally, the performance of the algorithm and the characteristics of vehicle information are comprehensively analyzed. The security of Internet of Vehicle's data transmission is efficiently improved by the proposed method.

Biometric identification on the cloud: A more secure and faster construction

Cloud-assisted biometric identification can benefit data owners with limited resources by offloading their storage and identification tasks to cloud servers with abundant resources. However, the adoption of this computing paradigm is hindered by security and privacy concerns. To solve the dilemma, several privacy-preserving cloud-assisted biometric identification protocols have been proposed. Nevertheless, the existing designs suffer from one or more of the following restrictions: (1) support only for data owner-online query, (2) low security level, and (3) inefficiency. Motivated by these challenges, this paper aims to explore a more secure and efficient construction. Concretely, we initialize the error weighted hashing (EWH)-based data packing technique to replace the time-consuming linear scan search during the identification process. Additionally, to ensure compatibility with the EWH-based search algorithm, we formulate an identity (id)-based scoring strategy and introduce a random split-based data encryption algorithm, which circumvents the costly homomorphic encryption scheme. Within the framework of the two non-colluding servers model, the random split-based one-time pad encryption guarantees provably indistinguishable security against chosen-plaintext attacks. Finally, since only simple hash and xor operations are involved, our novel construction exhibits significantly improved performance compared to prior designs, both in theory and in practice. This superiority is validated through rigorous theoretical analysis and extensive experimental evaluation.

Securing Social Media Imagery: GAN-Driven Encryption and CNN Analysis with DEA Protection

Generative adversarial networks (GANs) transform low-dimensional random noise into a photorealistic picture. The use of such misleading pictures laden with irrelevant information on social media platforms might lead to serious and challenging issues. The goal of this study is to create and identify the affected GAN pictures. Additionally, enhanced GAN detection accuracy by preprocessing and segmentation feature extraction. This study examines the effectiveness of several learning-based methods for detecting image-to-image translation. Generative adversarial networks use deep learning approaches like convolutional neural networks for generative modelling and the DEA (Data encryption standard) is used to produce the encryption key. To accurately identify fake images, an effective picture forgery detector is necessary. Recent advancements in generative adversarial networks (GANs) have been focused on producing photorealistic pictures quickly and successfully. However, GANs can complicate visual forensics and model attribution. Data from one source and pictures from another can have a wide range of uses, including in fields like computer vision, video, and language processing. The investigation of some photos reveals that both conventional and deep learning detectors may reach up to 95% detection accuracy. However, only deep learning maintains good accuracy on compressed data. An article that explains how to spot GAN-generated false images on social media also provides context on GANs and the theoretical concepts underlying them.

Hide Text within a Video Using Data Encryption Standard (DES) Technology

It is possible to encode a message using one of the encryption algorithms and embed the text within a selected frame from the video, as this is considered a more secure method for handling confidential information. The concealment is deemed unbreakable because the message is encrypted in a manner that prevents decryption, making it challenging to retrieve the hidden text. In this study, Data Encryption Standard (DES) technology was employed to embed the last bits of the chosen video frame using a software-based approach. The frames, decoders, PSNR, and encrypted messages are illustrated in various videos. The results demonstrated minimal impact on the video's accuracy, as concealment occurs in the least significant bits (LSB), preserving the overall precision of the video.

Encryption of Card Details Using AES with a 128-bit Key a Secure Approach to Data Protection

Data security is a critical concern in today's digital age, particularly regarding the protection of sensitive information such as cardholder data. This research paper explores the implementation of Advanced Encryption Standard (AES) encryption with a 128-bit key as a means of safeguarding card details. The project demonstrates the encryption and decryption process, ensuring data confidentiality and integrity. Utilizing the pycryptodome library in Python, the AES encryption technique is applied to encrypt card details, offering a secure approach to data protection. The methodology section outlines the AES encryption process, emphasizing key generation, encryption, and decryption. A detailed explanation of the Python implementation highlights the importance of key length and randomness in the encryption process. The results section presents the outcomes of encrypting and decrypting card details using AES encryption, analyzing the efficiency and security of the encryption process. Additionally, potential vulnerabilities and challenges encountered during implementation are discussed, along with their resolutions. In the discussion section, the research findings are interpreted in the context of data security and encryption standards. A comparison between AES encryption and other encryption algorithms is provided, evaluating performance and security aspects. Consideration is given to future research directions in the field of data encryption and security. Overall, this research paper underscores the significance of AES encryption with a 128-bit key for protecting cardholder information. By implementing AES encryption, organizations can enhance data security measures, ensuring the confidentiality and integrity of sensitive data in digital transactions. This abstract provides a concise overview of the research paper, summarizing the objectives, methodology, results, and implications of implementing AES encryption for card data protection. It highlights the importance of data security and encryption techniques in safeguarding sensitive information in digital transactions. Tokenizing cards enhances security, ensures regulatory compliance, boosts customer trust, and streamlines payment processes while reducing liability for organizations. Future developments may involve tokenizing cards even without customer consent. Keywords: Tokenization, Payment systems, Encryption, Fraud prevention

Organizations' readiness for insider attacks: A process‐oriented approach

AbstractContextOrganizations constantly strive to protect their assets from outsider attacks by implementing various security controls, such as data encryption algorithms, intrusion detection software, firewalls, and antivirus programs. Unfortunately, attackers strike not only from outside the organization but also from within. Such internal attacks are called insider attacks or threats, and the people responsible for them are insider attackers or insider threat agents. Insider attacks pose more significant risks and can result in greater organizational losses than outsider attacks. Thus, every organization should be vigilant regarding such attackers to protect its valuable resources from harm. Finding solutions to protect organizations from such attacks is critical. Despite the importance of this topic, little research has been conducted on providing solutions to mitigate insider attacks.ObjectiveThis study aims to develop an organizational readiness model to assess an organization's readiness for insider attacks.MethodWe conducted a multivocal literature review to identify practices that can be used to assess organizations' readiness against insider attacks. These practices were grouped into different knowledge areas of insider attacks for organizations. The insider attack readiness model was developed using identified best practices and knowledge areas: compliance, top management, human resources, and technical.ResultsThis model was evaluated at two levels—academic and real‐world environments. The evaluation results show that the proposed model can identify organizations' readiness against insider attacks.ConclusionThe proposed model can guide organizations through a secure environment against insider attacks.

Privacy-Preserving Image Storage on Cloud Using An Unified Cryptographic Authentication Scheme

With the proliferation of several cutting-edge technologies such as the Artificial Intelligence (AI), and Machine Learning (ML), Internet of Things (IoT), cloud technology is gaining colossal popularity in recent years. Despite the general publicity on the theme across the digital world, defending user data kept in the cloud database is the most decisive problem. Recent potential cyber attacks reveal that storing private images entails more unique care related to other types of information on the cloud. As the cloud customer who has kept their images has no control over their data the cloud service provider has to ensure better security against cyber threats. Cryptography algorithms are the best choice to secure pictorial data in the cloud. These techniques transform images into an inarticulate form to keep confidentiality over undependable and vulnerable social media .In this paper, we aim to propose an approach for improving image security on the cloud using cryptography algorithms. We developed a cohesive approach, called Unified Cryptographic Image Authentication (UCIA) to protect user images on a cloud platform. The proposed UCIA approach includes two phases: (i)UCIA engenders a cipher text through a Data Encryption Standard (DES) by providing a key and a message as input, and (ii)UCIA implements a Twofish algorithm to encipher the pictures by applying cipher text. The enciphered picture data is then stored in the cloud database and can be recovered when the customer requests it. The effectiveness of both enciphering and deciphering procedures are analyzed using the evaluation metrics including time for enciphering, deciphering, cloud storage, and enciphering throughput. Experimental results reveal the better performance and strength of the UCIA approach