Encryption Features Research Articles

A True Random Number Generator Design Based on the Triboelectric Nanogenerator with Multiple Entropy Sources.

The triboelectric nanogenerator (TENG) has the potential to serve as a high-entropy energy harvester, enabling the self-powered operation of Internet of Things (IoT) devices. True random number generator (TRNG) is a common feature of encryption used in IoT data communication, ensuring the security of transmitted information. The benefits of multiplexing TENG and TRNG in resource-constrained IoT devices are substantial. However, current designs are limited by the usage scenarios and throughput of the TRNG. Specifically, we propose a structurally and environmentally friendly design based on the contact-separation structure, integrating heat fluctuation and charge decay as entropy sources. Furthermore, filtering and differential algorithms are recommended for data processing based on TENG characteristics to enhance randomness. Finally, a TENG-based TRNG is fabricated, and its performance is verified. Test results demonstrate a random number throughput of 25 Mbps with a randomness test pass rate approaching 99%, demonstrating suitability for resource-constrained IoT applications.

Privacy preserving security using multi‐key homomorphic encryption for face recognition

AbstractRecently, face recognition based on homomorphic encryption for privacy preservation has garnered significant attention. However, there are two major challenges with homomorphic encryption methods: the security and efficiency of face recognition systems. We present a more efficient and secure PUM (Privacy preserving security Using Multi‐key homomorphic encryption) mechanism for facial recognition. By integrating feature grouping with parallel computing, we enhance the efficiency of homomorphic operations. The use of multi‐key encryption ensures the security of the facial recognition system. This approach improves the security and speed of facial recognition systems in cloud computing scenarios, increasing the original 128‐bit security to a maximum of 1664‐bit security. In terms of efficiency, comparing encrypted images takes only 0.302 s, with an accuracy rate of 99.425%. When applied to a campus scenario, the average search time for a facial template library containing 700 encrypted features is approximately 1.5 s. Consequently, our solution not only ensures user privacy but also demonstrates superior operational efficiency and practical value. In comparison to recently emerged ciphertext facial recognition systems, our solution has demonstrated notable enhancements in both security and time efficiency.

Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

Content-Based Image Retrieval (CBIR) uses complicated algorithms to analyze visual attributes and retrieve relevant photos from large databases. CBIR is essential to a privacy-preserving feature extraction and protection method for outsourced picture representation. SecureImageSec combines essential methods with the system’s key entities to ensure secure, private and protected image feature processing during outsourcing. For a system to be implemented effectively, these techniques must be seamlessly integrated across critical entities, such as the client, the cloud server that is being outsourced, the component that protects secure features, the component that maintains privacy in communication, access control, and authorization, and the integration and system evaluation. The client entity initiates outsourcing using advanced encryption techniques to protect privacy. SecureImageSec protects outsourced data by using cutting-edge technologies like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). Cloud servers hold secure feature protection entities and protect outsourced features’ privacy and security. SecureImageSec uses AES and FPE to protect data format. SecureImageSec’s cloud-outsourced privacy-preserving communication uses SSL/TLS and QKD to protect data transmission. Attribute-Based Encryption (ABE) and Functional Encryption (FE) in SecureImageSec limit access to outsourced features based on user attributes and allow fine-grained access control over decrypted data. SecureImageSec’s Information Leakage Rate (ILR) of 0.02 for a 1000-feature dataset shows its efficacy. SecureImageSec also achieves 4.5 bits of entropy, ensuring the encrypted feature set’s muscular cryptographic strength and randomness. Finally, SecureImageSec provides secure and private feature extraction and protection, including CBIR capabilities, for picture representation outsourcing.

Multiple-state pattern encrypted superhydrophobic-icephobic colorimetric sensing film for anticounterfeiting and food freshness monitoring

Exploring high-sensitive and convenient anticounterfeiting as well as food freshness monitoring technologies to combat food safety is urgently needed but remains a great challenge. Herein, the color dynamic response and transparency encryption features of colorimetric label with heteronetwork structure can effectively cope with the above problems. The colorimetric label demonstrates excellent superhydrophobic with water contact angle of 156.2° and roll-off angle of 8.9°, and icephobic that delays the freezing time of 40 μL water droplets from 38 s to 162 s and reduces the bonding strength of ice from 249.00 kPa to 15.20 kPa. Furthermore, the colorimetric label exhibits a colorimetric shift of 126 nm and a detection limit of 0.8 ppm for NH3, making it successfully applicable for shrimp freshness monitoring. Notably, a locking encryption system featuring multiple logics can be achieved by combining the colorimetric properties and transparency changes of the colorimetric tags. This work proposes an ingenious, cost-effective, and convenient dual system for anticounterfeiting and food freshness monitoring, which inspired the development of an intelligent monitoring system for food quality, and has significant implications for enhancing food safety.

An Electronic Voting Scheme with Privacy Protection

Privacy protection is one of the key focuses of electronic voting technology, and the privacy information of voting participants is particularly important, such as their personal identity information and election voting results. Protecting the privacy information of participants can ensure the fairness and trustworthiness of voting activities. When designing and implementing electronic voting systems, privacy protection issues must be fully considered. In this paper, an electronic voting scheme based on ElGamal homomorphic encryption properties is proposed. The scheme utilizes the feature of homomorphic encryption to encrypt the voting results of participants and decrypt the voting results to obtain the final data. This scheme can to some extent improve the anonymity of voting participants and ensure the security of voting data by the vote counting center.

Light and Humidity Dual-Responsive Anti-Counterfeiting Films Based on Hydrogen-Bonded Cholesteric Liquid Crystal Polymers with Spiropyran.

There is still significant room for improvement when combining structural color with fluorescence patterns in dual anti-counterfeiting and dynamic anti-counterfeiting labels. In this study, we achieved significant breakthroughs under dual anti-counterfeiting conditions by using the structural color properties of the hydrogen-bonded cholesteric liquid crystal (HBCLC) and combining them with the fluorescence dye spiropyran (SP) to create anti-counterfeiting patterns. The anti-counterfeiting label can only display storage information after meeting the conditions of humidity and ultraviolet light (UV) and has the functions of dynamic encryption and repeated reading. We adjusted the center of the reflection band of the HBCLC film to transition from red to infrared under 40-90% relative humidity (RH) conditions and used it as a background film to draw anti-counterfeiting patterns with SP. Since these fluorescence dyes can switch between merocyanine (MC) (red) and SP (colorless) under UV and visible light conditions, when combined with the HBCLC, orthogonal dynamic encryption was achieved. Additionally, with the adsorption of SP, the reflection band of HBCLC films under the same humidity range increased from around 160 nm to around 260 nm, greatly improving the sensitivity to humidity changes. Furthermore, under UV conditions, it can still emit red fluorescence, demonstrating a polymorphic encryption feature, which greatly increased the complexity of the anti-counterfeiting pattern with significant significance to dynamic anti-counterfeiting and information storage.

Identifiable, But Not Visible: A Privacy-Preserving Person Reidentification Scheme

Person re-identification (Person Re-ID) is widely regarded as a promising technique to identify a target person through surveillance cameras in the wild. Nevertheless, person Re-ID leads to severe personal image privacy concerns as personal images are stipulated by laws and guidelines as private data. To address these concerns, this article explores the first solution for building a privacy-preserving person Re-ID system. Specifically, this article formulizes privacy-preserving person Re-ID as similarity metrics of encrypted feature vectors because the underlying operation of person Re-ID is to compute the similarity of feature vectors that are extracted from person images by a machine learning model. However, feature vectors are generally denoted by floating-point numbers. To this end, this article exploits a series of new encoding mechanisms and secure batch computing protocols to encrypt floating-point feature vectors and achieve the underlying operation of person Re-ID. Rigorous theoretical analyses demonstrate that this work achieves person Re-ID without compromising any personal image privacy. Furthermore, the proposed secure batch protocols significantly enhance the performance of privacy-preserving person Re-ID while outputting the same precision as the previous method.

Wearable Integrated Self‐Powered Electroluminescence Display Device Based on All‐In‐One MXene Electrode for Information Encryption

AbstractAnti‐counterfeiting and visual optical information encryption/decryption technology have attracted widespread attention in the field of information security. Luminescent encryption technologies still face a huge challenge in external high voltage power supply, complex architecture, and expensive decryption equipment, which hinder their broad applications. Herein, a wearable integrated self‐powered electroluminescent (EL) display device (W‐ELD) that consists of MXene/Silicone‐based triboelectric nanogenerator (MS‐TENG) and EL device based on a shared MXene electrode is developed for patterned display and information encryption. The W‐ELD features an all‐in‐one MXene electrode with excellent flexibility and high conductivity of 0.6 kΩ sq−1, which is shared by both MS‐TENG and EL devices. The MS‐TENG demonstrates excellent output performances (output power of 0.9 Wm−2) and high stability and durability (104 cycles), which can directly light up the flexible patterned EL device. More importantly, when dripping conductive electrolyte solution, the W‐ELD based on “中國”‐patterned MXene electrode can precisely reveal the encryption information through self‐powered EL emission for real‐time visualized information interaction. Consequently, the all‐in‐one MXene electrode‐based W‐ELD that integrates both MS‐TENG and EL device demonstrates exceptional patterned EL‐based information encryption features, which offers a potential prospect in wearable self‐powered optoelectronic devices, flexible displays, and encryption technology.

Robust Hashing via Global and Local Invariant Features for Image Copy Detection

Robust hashing is a powerful technique for processing large-scale images. Currently, many reported image hashing schemes do not perform well in balancing the performances of discrimination and robustness, and thus they cannot efficiently detect image copies, especially the image copies with multiple distortions. To address this, we exploit global and local invariant features to develop a novel robust hashing for image copy detection. A critical contribution is the global feature calculation by gray level co-occurrence moment learned from the saliency map determined by the phase spectrum of quaternion Fourier transform, which can significantly enhance discrimination without reducing robustness. Another essential contribution is the local invariant feature computation via Kernel Principal Component Analysis (KPCA) and vector distances. As KPCA can maintain the geometric relationships within image, the local invariant features learned with KPCA and vector distances can guarantee discrimination and compactness. Moreover, the global and local invariant features are encrypted to ensure security. Finally, the hash is produced via the ordinal measures of the encrypted features for making a short length of hash. Numerous experiments are conducted to show efficiency of our scheme. Compared with some well-known hashing schemes, our scheme demonstrates a preferable classification performance of discrimination and robustness. The experiments of detecting image copies with multiple distortions are tested and the results illustrate the effectiveness of our scheme.

The Application of Fully Homomorphic Encryption on XGBoost Based Multiclass Classification

Fully Homomorphic Encryption (FHE) is a ground breaking cryptographic technique that allows computations to be performed directly on encrypted data, preserving privacy and security. This paper explores the application of Fully Homomorphic Encryption on Extreme Gradient Boosting (XGBoost) multiclass classification, demonstrating its potential to enable secure and privacy-preserving machine learning. The paper presents a framework for training and evaluating XGBoost models using encrypted data, leveraging FHE operations for encrypted feature engineering, model training, and inference. The experimental results showcase the feasibility of applying Fully Homomorphic Encryption to XGBoost-based multiclass classification tasks while maintaining data confidentiality. The findings highlight the trade-off between computation complexity and model accuracy in FHE-based approaches and provide insights into the challenges and future directions of utilizing Fully Homomorphic Encryption in practical machine learning scenarios. The study underscores the significance of privacy-preserving machine learning techniques and paves the way for secure data analysis in sensitive domains where data privacy is of utmost importance.

Kriptografi dan Penerapannya Dalam Sistem Keamanan Data

An understanding of what cryptography is, to understand cryptography and how it is applied in data security, so that data will be safer and more protected from spamming or illegal hacking of information. Observations of cryptography and its application to data security are carried out using the Approach of Literary Studies. Library Studies is a data collection technique by studying various reference books and the results of previous similar research, which is useful for obtaining a theoretical foundation on the problem to be studied. Cryptography is a technique of conveying messages in a hidden manner by utilizing data encryption features, which serve to secure data, whether transferred through a computer network or not, which is very useful for protecting, privacy, data integrity, authentication, and non-repudiation. Nowadays the use of cryptography has begun to be widely used in everyday life. Use cryptographic systems for the security of your data when accessing information.

A Secure Messaging Application with Unbreakable End to End Encryption

Abstract: Instant messaging services on mobile devices, such as WhatsApp, have become immensely popular, largely due to their end-to-end encryption (E2EE) feature, which ensures user privacy. However, this has raised concerns for some governments who argue that E2EE makes it challenging to combat terrorism and organized crime. These governments have expressed the desire for a "backdoor" to access messages in cases of credible threats to national security. However, WhatsApp users have strongly opposed this idea, citing concerns about privacy infringement and potential exploitation by hackers. This paper presents the advantages of maintaining E2EE in WhatsApp and argues against granting governments a "backdoor" to access user messages. It highlights the benefits of encryption in safeguarding consumer security and privacy, while also acknowledging the challenges it poses to public safety and national security. In the realm of internet messaging security, cryptography plays a crucial role in protecting networks. This paper aims to raise awareness among common computer users about the importance of messaging security and its requirements. Several cryptographic techniques have been developed to achieve secure communication, and the proposed messaging system ensures security in accordance with standard security models

Implementasi Secure Storage Menggunakan Metode Full Disk Encryption dan Tamper Proof pada Cloud Storage

Currently, storage development has shifted to internet-based storage, or cloud storage, as online storage media. Cloud storage is becoming more popular among the public due to several advantages, including the fact that when the storage on personal computers runs out, users do not need to purchase new storage devices or incur additional costs for device maintenance. However, using such online storage can increase risks such as unauthorized access, data leakage, sensitive data, and privacy rights. This study proposes a prototype secure personal cloud storage system with physical database protection. The device has data encryption features that use full disk encryption to secure data on the server, VPN to secure communication between user and server, and tamper proof to physically secure the database. The test results show that the implementation of secure storage affects the performance of cloud storage, as evidenced by the decrease in data write speed from 19.87 Mb/s to 15.75 Mb/s after the implementation of LUKS full disk encryption. On the security side of data transmission, OpenVPN can secure the transmission between the user and the server. This is evidenced by sniffing testing using wireshark tools which shows that data transmission has been encapsulated by the OpenVPN application. In unit testing carried out on LUKS, it proved that the number of LUKS version 2 key slots corresponds to the reference, which is a maximum of 32 key slots. The last test proves that the tamper proof mechanism can work as expected. The tamper proof mechanism works when the device detects an indication of an attack on the physical device. The tests carried out were destroying the device casing using a hammer, opening the casing bolt, and a saw. The test results show that the device will perform damage to the SSD when the attack indication exceeds the tolerance value.

E2EE Web Messaging Application Using Cryptography Techniques

Abstract: Instant messaging services on mobile devices, such as WhatsApp, have become immensely popular, largely due to their end-to-end encryption (E2EE) feature, which ensures user privacy. However, this has raised concerns for some governments who argue that E2EE makes it challenging to combat terrorism and organized crime. These governments have expressed the desire for a "backdoor" to access messages in cases of credible threats to national security. However, WhatsApp users have strongly opposed this idea, citing concerns about privacy infringement and potential exploitation by hackers. This paper presents the advantages of maintaining E2EE in WhatsApp and argues against granting governments a "backdoor" to access user messages. It highlights the benefits of encryption in safeguarding consumer security and privacy, while also acknowledging the challenges it poses to public safety and national security. In the realm of internet messaging security, cryptography plays a crucial role in protecting networks. This paper aims to raise awareness among common computer users about the importance of email security and its requirements. Several cryptographic techniques have been developed to achieve secure communication, and the proposed messaging system ensures security in accordance with standard security models.

CompressKey—Near Lossless Layout Compression and Encryption Using Convolutional Auto-Encoder Model and Expansion-Reduction Pattern Techniques

Malicious manipulation of very large-scale integration physical-layout design is a serious problem in modern integrated circuit design. The physical-layout design database requires a highly compressed secured storage medium. In this article, we propose a secured compressive asymmetrical convolutional auto-encoder (ACAE) machine learning framework, CompressKey, which performs layout compression and encryption simultaneously. It utilizes geometric features to eliminate redundancies in layout patterns. We propose a “Divide and Merge” technique to partition each layer into smaller sizes of unique patterns to address the inconsistency of layout pattern complexity. We also propose “Matrix Expansion” and “Matrix Reduction” techniques on the matrix-based pattern to achieve secured “near lossless” compression on the layouts. We have evaluated CompressKey on 14/28/32 nm open-source ICCAD contest databases and achieved a secured compression ratio of 4.54 with encryption features outperforming <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula> compared to the state-of-the-art techniques.

Novel secure data protection scheme using Martino homomorphic encryption

Globally, data maintenance and its security are vital tasks due to the rapid development of advanced technologies. Users now utilise Cloud computing technique and security techniques to preserve their data securely from intruders and hackers. Even yet, because of the technology's rapid advancement and inherent insecurity, attackers are conducting assaults on the cloud data. Hence, a homomorphic encryption technique was proposed based on Matrix Transformations with shifts, rotations, and transpositions of Binary converted ASCII values of each character in the plain text. For both encryption and decryption, symmetric cryptography employs the same secret key. The “avalanche effect” is a desirable feature of symmetric encryption in which two distinct keys generate separate cipher texts for the same message. As there are different conditions for the key, it helps to achieve this effect in this technique. The suggested algorithm's cryptanalysis reveals that it is more powerful than the existing encryption methods and resistant to a variety of attacks. So that an attacker cannot easily predict a plaintext through a statistical analysis.

Query extraction based on encrypted features for CBIR from cloud

Due to the increasing use of mobile devices, content-based image retrieval is gaining massive popularity as mobile devices run on batteries. It cannot perform heavy image processing computation, so mobile users can extract just image features and offload them to the cloud. The cloud will perform content-based image searches and return a search result. COREL10K images dataset was selected, and VGG16, RESNET, DENSENET, and MOBILE NET algorithms were applied. Finally, the method with the best performance is preferred. Also, PAHE (Packed Additive Homomorphic Encryption) algorithm is selected to encrypt the feature vectors, and image similarity calculations are done with Secure Multi-party Computation (SMC). The following parameters are used for each algorithm to calculate accuracy, precision, recall, Jaccard index, error rates, and F1-score. The algorithms are then compared and selected the best algorithm. The best model obtained is VGG16, with the highest accuracy. The procedure can help Mobile users perform content-based image searches by extracting features and then uploading them to the cloud.

A Strategic Approach to Design Multi-Functional RGB Luminescent Security Pigment Based Golden Ink with Myriad Security Features to Curb Counterfeiting of Passport.

Authentication and verification of the most important government issued identity proof, i.e. passport has become more complex and challenging in the last few decades due to various innovations in ways of counterfeiting by fraudsters. Here, the aim is to provide more secured ink without altering its golden appearance in visible light. In this panorama, a novel advanced multi-functional luminescent security pigment (MLSP) based golden ink (MLSI) is developed that provides an optical authentication and information encryption features to protect the legitimacy of the passport. The advanced MLSP is derived from the ratiometric combination of different luminescent materials to form a single pigment which emits red (620 nm), green (523 nm) and blue (474 nm), when irradiated via 254, 365 and 980 nm NIR wavelengths, respectively. It also includes magnetic nanoparticles to generate magnetic character recognition feature. The MLSI has been fabricated to examine its printing feasibility and stability over different substrates using the conventional screen-printing technique against harsh chemicals and under different atmospheric conditions. Hence, these advantageous multi-level security features with golden appearance in visible light is a new breakthrough toward curbing the counterfeiting of passport as well as bank cheques, government documents, pharmaceuticals, military equipment, and many more.

Facial Image Encryption for Secure Face Recognition System

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

Blockchain for baccalaureate examination sheets protection in Iraq

&lt;span lang="EN-US"&gt;Due to their unique qualities, including data integrity, security, decentralization, and dependability, blockchain technologies have drawn a lot of interest from both academia and business. In order to effectively handle the adoption problems of blockchain technologies, it is important to identify what influences their use and adoption. In order to protect the exam sheet (as image format) for the baccalaureate examination in Iraq. This paper article sought to construct a private blockcahin powered by a variety of algorithms, After that, with this concern about privacy and fear of private information leaking, and in order to ensure that that image will only be seen by the specific person you want, the technology is full of many encryption features that achieve this end, we encrypt the image before sending it as a transaction in the Blockchain network. Also, we obtained speed smart contact verifications compared with traditional systems due to the proposed system is private and has simple complexity.&lt;/span&gt;