Encrypted Content Research Articles

Efficient Post-Quantum Pattern Matching on Encrypted Data

Pattern matching methods are essential in various applications where users must disclose highly sensitive information. Among these applications are genomic data analysis, financial records inspection, and intrusion detection processes, all of which necessitate robust privacy protection mechanisms. Balancing the imperative of protecting the confidentiality of analyzed data with the need for efficient pattern matching presents a significant challenge. In this paper, we propose an efficient post-quantum secure construction that enables arbitrary pattern matching over encrypted data while ensuring the confidentiality of the data to be analyzed. In addition, we address scenarios where a malicious data sender, intended to send an encrypted content for pattern detection analysis, has the ability to modify the encrypted content. We adapt the data fragmentation technique to handle such a malicious sender. Our construction makes use of a well-suited Homomorphic Encryption packing method in the context of fragmented streams and combines homomorphic operations in a leveled mode (i.e. without bootstrapping) to obtain a very efficient pattern matching detection process. In contrast to the most efficient state-of-the-art scheme, our construction achieves a significant reduction in the time required for encryption, decryption, and pattern matching on encrypted data. Specifically, our approach decreases the time by factors of 1850 , 10 6 , and 245 , respectively, for matching a single pattern, and by factors of 115 , 10 5 , and 12 , respectively, for matching 2 10 patterns.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

Quality and recognizability estimation video encryption database

In literature about selective encryption of image and video content, image quality indices are usually used to gauge the degree of encryption. These methods have frequently been shown not to work well for the evaluation of encryption, mainly due to them being trained on predominantly high quality contents. The problem for creating a proper recognition index or visual encryption strength index is the lack of data to train on. In this paper we present the first database of encrypted video content, ranging from high quality to completely unrecognizable, together with human observer scores for quality and recognizability. We also provide a basic evaluation of visual quality indices on this database, directly and in different combination by fusion, to showcase that currently image and video quality indices are ill fit for the purpose of estimating video encryption strength/recognizably. This inability of quality indices to perform also showcases that this database fills the required blind spot of currently available data.

Securing Decentralized Storage in Blockchain: A Hybrid Cryptographic Framework

Abstract The evolution of decentralized storage, propelled by blockchain advancements, has revolutionized data management. This paper focuses on content security in the InterPlanetary File System (IPFS), a leading decentralized storage network lacking inherent content encryption. To address this vulnerability, we propose a novel hybrid cryptographic algorithm, merging AES 128-bit encryption with Elliptic Curve Cryptography (ECC) key generation. The algorithm includes ECC key pairs, random IV generation, and content/AES key encryption using ECC public keys. Benchmarking against standard AES 256-bit methods shows a significant 20% acceleration in encryption speed and a 16% increase in decryption efficiency, affirming practicality for enhancing IPFS content security. This research contributes to securing decentralized storage and provides a performance-driven solution. The promising results highlight the viability of the proposed approach, advancing understanding and mitigating security concerns in IPFS and similar systems.

EFFICIENT MULTI-AUTHORITY ACCESS CONTROL USING CIPHERTEXT POLICY -HIERARCHICAL ATTRIBUTE BASED ENCRYPTION IN CLOUD STORAGE

The goal of this research is to improve the security of cloud-based data storage systems by presenting the Cloud Secure Storage Mechanism based on Data Dispersion and Encryption (CPHABE). The User Frame, Authority Login, Cloud Server Login, and Data Owner Login are the four separate components that make up the system. By providing file names and requesting transformation keys from the authority for decryption, users start downloads. Requests for transformation keys, upload information, and key creation are handled by the authority. Owner identities, file names, encrypted content, gate types, and storage size graphs are all managed by cloud servers. Partially decrypted data, such as user identities, file names, transformation keys, data owner IDs, and partially decrypted content, is sent in response to user requests. Data owners may use the system to explore files, obtain keys, and apply hybrid encryption methods including Authenticated Encryption (AE) and Key Encapsulation Mechanism (KEM). This all-encompassing strategy guarantees strong encryption, data dispersion, and security measures, offering a stable foundation for cloud storage systems. Keywords: Cloud Storage, Security Mechanism, Encryption

A Multilayer Data Security Using Matrix Transformation and RSA For Public Cloud Storage

Cloud computing is an internet-based system that allows clients to access computers, software, infrastructure, devices, and other resources through a subscription model. Data security is a major concern for the cloud computing model. Data security is a research challenge in any cloud environment. When cloud users upload their confidential and secret data through the cloud, the security of this data must be ensured. In order to improve data security, a new method is presented in this paper. The approach uses ten random prime integers to compute the public and safe private keys after transforming user data into a matrix format. The encrypted content is then decrypted using the secured private key, improving total data security. The Hackman tool is used to analyse the encryption and decryption times as well as the security level. The OPNET tool measures the power of encryption and decryption. The findings indicate that all five potential file sizes for the proposed algorithm's parameters have the highest value. The cypher text was analysed using this suggested technique using brute force and dictionary attacks. This method is more effective, more secure, and impenetrable.

PETNet: Plaintext-aware encrypted traffic detection network for identifying Cobalt Strike HTTPS traffics

Cobalt Strike is the most prevalent attack tool abused by cyber-criminals to achieve command and control on victim hosts over HTTPS traffics. It appears in many ransomware attacks and espionage attacks, threatening public privacy and national security. Therefore, it is of significant value to detect Cobalt Strike HTTPS traffics effectively. However, existing methods could be easily deceived by variable infrastructures or disguised certificates used by attackers, or do not adequately capture the multi-aspect information and their interrelations in encrypted traffics. To overcome these limitations, in this paper, we propose a Plaintext-aware Encrypted Traffic Detection Network (PETNet) to identify Cobalt Strike HTTPS traffics, which contains three main modules: (1) Meta Information Modeling, which parses handshake payloads into semantically explicit identity-agnostic meta features, avoiding being disturbed by infrastructures or certificates; (2) Sequential Information Modeling, which models the interaction between attackers and victims via a Transformer encoder, and captures the interrelations among multi-aspects of traffics by a meta-information-guided attention mechanism, realizing configuration-aware encoding of encrypted contents; (3) Fusion & Prediction, which fuses the interrelated meta information and sequential information to make the final prediction. We conduct extensive experiments on a close-world and four open-world datasets. PETNet outperforms the best baseline by 53.42% in F1-score on average, and remains robust to the concept drift issue during the test period of 14 months, proving its effectiveness and generalization ability.

End-to-end data security with DMaya on IPFS: keyless secured private swarm for the closed user group

The interplanetary File system (IPFS) provides data communication using plaintext over distributed and decentralized systems. Up till now, the security of data has mostly been established using keys. To facilitate classified data communication within a Closed User Group (CUG), services of IPFS private swarm are required to render additional content encryption. Currently, there is no inherent security built into IPFS. This paper presents an application DMaya on IPFS to provide keyless encryption on IPFS based on the Quasigroup-based Secret Sharing Scheme (QSS). IPFS is used as a storage medium, irrespective of the nature of data. Data security in decentralized mode with IPFS is provided with DMaya. The classified data of all types (i.e. video data, audio data, text data, and image data) when subjected to DMaya application, are fragmented into smaller fragments then processed by the probabilistic Quasigroup-based Secret Sharing (QSS) Scheme. DMaya keyless encryption generates the metadata tag file at the source node. The recipient node within the same private IPFS swarm, on receipt of metadata tag file reconstructs the original data with DMaya application.

Generalization of multicast encryption for Internet of Things deployment

The problem of multicast encryption has been a focus of attention in the Internet of Things environment for the last decade, in which one central server generates and securely distributes a common key designated for a group of IoT devices. These devices can establish secure communication by sending encrypted content within the group using the earlier established group key. It is crucial that the encrypted content can be decrypted only by the member devices. An issue arises when the number of multicast groups increases, and devices tend to be part of various groups simultaneously. Hence we consider a generalization of multicast encryption where multiple multicast groups exist in the IoT environment that are not independent. That means the involvement of an IoT device is not restricted to only one group, which was the case earlier. In our study, the parameters of interest are computational overhead per device, the number of keys stored, and the bandwidth required per transmission as a function of the total number of devices d and the number of groups m. As our main result, we considered two cases in framing efficient and secure solutions for the generalization of multicast encryption. Since several solutions are already proposed for simple multicast encryption techniques, first is the case where it is assumed that a prior optimal solution is already known for multicast encryption and how to build a multi-group scenario out of this. Second, if this situation is not there, we need to define the generalized problem for multicast encryption, which includes overlapped multiple multicast groups. Henceforth, this paper defines important security theorems and their proofs for both cases, along with security models and strong security definitions.

A Survey of Internet Performance During COVID-19

Background Since the beginning of 2020, many societal systems have been used to extend the health care system, which were not planned for, and as such, there is concern for its collapse. Clearly, the collapse of the health care system, primarily hospitals, has been a key concern, and many initiatives, including lockdown and curfew, were taken to avoid such a collapse. The internet was the key platform used to enable people to work from home, provide remote teaching, conduct meetings on the web, etc. However, when it comes to data communication and processing, the risk of collapse is not the only risk, and maybe not even the biggest one. Many systems were not properly adapted for used in such a hurry, which did not allow time (and concern) for a proper risk and privacy assessment. Objective This paper presents internet performance statistics and analyzes how this knowledge can be used in future designs of internet-based telemedical solutions. Methods Statistics regarding traffic increases and security attacks on the internet during 2020 and 2021 were analyzed. Results The internet did not collapse during the COVID pandemic—as many people had predicted. However, the massive use of the internet, in new innovative ways, created a number of new opportunities for cybersecurity breach. Especially, the use videoconferences enabled made-in-middle attacks, phishing, and other classical breaches in new ways due to insufficient authentication and content encryption. Conclusions Even though a large amount of experience has been gathered with respect to scaling eHealth systems, a minimum amount of improvement with respect to privacy and security has been identified. Conflicts of Interest None declared.

A Blockchain Based Decentralized NFT Marketplace

With the advancement of technology around the globe lead to a rise of technology called Blockchain and amazing technology that completely eradicates the centralized network. Blockchain Technology has got its applications from finance, gaming, supply-chian etc. One of Applications of this amazing technology are NFT’s(Non Fungible Token’s) That act as a digital assets in the world of Blockchain. NFT’s can be any form of data from art, music to video etc. The interest of NFT’s have been growing in every field of various industries like fashion, gaming, etc. Non-fungible tokens (NFTs) are transferrable rights to digital assets, such as art, in-game items, collectables, or music. The phenomenon and its markets have grown significantly since early 2021.The information about the NFT’s are stored onto the blockchain Where each information is kept encrypted and prevented from attack as its impossible to alter the data in the blockchian. This cutting-edge technology continues to grow and capture the attention of the masses as more applications of NFT’s are identified with time. The System proposed in this paper allows consumers to transfer encrypted content and write it to NFT’s. Various consumers can approach the content of NFT’s by mentioning their purchase or endorsement. Confidential information is licensed for a period of time, after which the information is appropriately deleted.

Implementation of a Secure LoRaWAN System for Industrial Internet of Things Integrated With IPFS and Blockchain

Low-power, low-cost, and long-range connectivity for the Industrial Internet of Things (IIoT) networks are the key stipulations, nowadays. However, implementing a cost-effective, flexible, and feasible system considering server and networking security is still an open challenge. In this article, a complete end-to-end long-range wide area network (LoRaWAN) system has been demonstrated by implementing blockchain-based secure distributed data management, which is applicable in various secure IIoT applications. Dynamic data collected by multiple LoRa sensors are encrypted in a LoRa server, and the encrypted content is automatically stored in the InterPlanetary file system (IPFS) to ensure data confidentiality, integrity, and availability. To achieve data consistency, the content IDs collected from the IPFS are stored in the quorum blockchain with consortium setup using a smart contract. The consortium network is maintained by the Raft consensus algorithm employing seven nodes. The design architecture of the hardware used for both LoRa transmitting node and gateway has been described in comprehensive manners. The performance of the LoRaWAN system is analyzed by the received signal strength indicator, the communications range, and packet loss rate metrics in both line-of-sight and nonline-of-sight test systems. The data management scheme is implemented in Python, and the performance is evaluated in terms of transaction time and block size.

High-capacity reversible data hiding in encrypted 3D mesh models based on multi-MSB prediction

Reversible data hiding in encrypted domain (RDH-ED) technology performs content encryption to guarantee the privacy of the original media and hide additional data for multimedia management or access control. However, for three dimensional (3D) model, it is still challenging to improve data embedding capacity under the premise of ensuring reversibility. This paper proposes an improvement RDH-ED method for 3D models. Firstly, the vertices are classified into “embedded set” and “prediction set” based on the odd-even property of indices. Multiple most significant bit (multi-MSB) of each vertex is predicted adaptively and marked in the original model to reserve room for data embedding. To further free up the room, the auxiliary information used for data extraction and model recovery can be compressed by entropy coding. Finally, the correlation between two sets is used to recover vertices that were modified due to data embedding. Experimental results show the proposed method significantly improves the embedding capacity compared with state-of-the-art methods.

Dual Layer Encryption for IoT based Vehicle Systems over 5G Communication

In modern communication scenario of the 5G era, the service quality is the greatest concern for the users. Also, the concept of security can’t be neglected in this case. In the IoT oriented services like vehicle and VANET systems, the security in the presentation layer of the network is required. This work is over the security mechanism of the service storage and fetching the files for service. A new scheme of multi layered file and content encryption has been produced in order to strengthen the security of the file and data to maintain integrity and confidentiality of the IoT enabled services implemented in 5G. The encryption scheme is designed for the password encryption through asymmetric key cryptography (RSA) along with an enhanced concern of internal content or data security with symmetric key (AES-128) cryptography. This encryption system of double layer for a file makes the study unique and differentiable than other security schemes.

Wireless Sensor Network Security Based on Improved Identity Encryption

In order to protect network information security and improve the security of wireless sensor networks, based on chaotic systems, we propose a wireless sensor algorithm based on improved identity encryption. First, the basic principle of chaotic system mapping is specifically analyzed; the two chaotic mapping systems are rectified to obtain the hybrid chaotic mapping system according to the demand of wireless sensing network. After that, an encryption framework and key are designed and the hybrid mapping system is applied to the encryption framework to encrypt the data. In this way, the length of the encryption algorithm is lengthened and the defensibility of the encrypted content is improved. Finally, the performance of the proposed encryption algorithm is tested in terms of information entropy, statistical methods, and ciphertext randomness. The test results show that compared with other encryption algorithms, the running speed of the encryption algorithm proposed in this paper is only 18.51 ms, which is faster than that of other encryption algorithms, and the memory consumption is only 27%, which is much lower than that of other algorithms. It can be seen that the proposed encryption algorithm has strong encryption effect and superior algorithm performance for data encryption in wireless sensor networks.

Industrial network-based behavioral anomaly detection in AI-enabled smart manufacturing.

Existing manufacturing systems are isolated from the outside world to protect their sites and systems. However, following the trend of the 4th Industrial Revolution, manufacturing systems have also increased the connectivity of various domains and the convergence of numerous technologies. These systems are referred to as smart manufacturing systems. However, this trend has increased the challenge of network anomaly detection methods, which are a major approach to network security in smart manufacturing. Existing methods define normality under the premise that network components are static, and network operation is periodic compared to the information technology environment. Therefore, comprehensive and volatile network environments require significant time, cost, and labor to define normality. Consequently, artificial intelligence (AI)-based anomaly detection studies have been actively conducted to solve this problem. However, such studies require manual analysis based on expert knowledge of each site during the preprocessing stage to extract the learning features from the collected network data. To solve the above problems, this study proposes a protocol reverse engineering method corresponding to the preprocessing stage of exiting AI studies. Through this method, existing AI-based anomaly detection studies can directly use the collected network data to learn normality without expert knowledge of the site. Furthermore, non-polling or reporting network operating environments that are rarely studied in the manufacturing security domain are targeted. Finally, we propose an anomaly detection method that uses an external signature, time information, the pattern of time intervals, and classified messages. Thus, the proposed method can detect anomalies in the encrypted contents of the manufacturing protocols.

Authorized Keyword Search over Outsourced Encrypted Data in Cloud Environment

For better data availability and accessibility while ensuring data secrecy, end-users often tend to outsource their data to the cloud servers in an encrypted form. However, this brings a major challenge to perform the search for some keywords over encrypted content without disclosing any information to unintended entities. This paper proposes a novel expressive authorized keyword search scheme relying on the concept of ciphertext-policy attribute-based encryption. The originality of the proposed scheme is multifold. First, it supports the generic and convenient multi-owner and multi-user scenario, where the encrypted data are outsourced by several data owners and searchable by multiple users. Second, the formal security analysis proves that the proposed scheme is semantically secure against chosen keyword and outsiders keyword guessing attacks. Third, an interactive protocol is introduced which avoids the need of any secure-channels between users and service provider. Fourth, due to the concept of bilinear-map accumulator, the system can efficiently revoke users and/or their attributes, and authenticate them prior to launching any expensive search operations. Fifth, conjunctive keyword search is provided thus enabling to search for multiple keywords simultaneously, with minimal cost. Sixth, the performance analysis shows that the proposed scheme outperforms closely-related works.

Low Quality and Recognition of Image Content

Assessment of visual encryption of video and image content requires a reliable estimation of content recognizability and low quality. As pointed out in the literature, current methods are insufficient and research into this topic, as well as into the relation between low quality and recognizability, is still lacking. This lack of research is primarily due to a lack of data. To improve on the status-quo we have taken a recognizability database and performed a subjective quality evaluation on a subset of the images. This gives us a new database with both subjective recognizability and quality information and allows to delve into the relation between low quality and recognizability. We analyze the relationship between quality and recognizability as well as the predictive quality of state of the art visual quality indices. We show that the visual quality indices are poor indicators for the estimation of recognizability. Furthermore, we show that they must be a poor fit because of the disparity between two distinct perceptual tasks: quality and recognizability.

Blockchain and NFTs for Time-Bound Access and Monetization of Private Data

Digital data has enabled organizations to anticipate future threats, opportunities, and trends. However, digital data owners do not know how their data is accessed, shared, and monitized. In this paper, we propose using blockchain technology and non-fungible tokens (NFTs) to enable time-bound access and monitization of private data. Our approach allows users to upload encrypted content and mint it into NFTs. Other users can access the NFTs’ content by requesting a purchase or a license. Purchasing content transfers the ownership of the NFTs to the buyer; whereas, licensing them permits accessing the private data for a limited period of time, after which the data gets automatically deleted. Our developed approach uses the decentralized application (DApp), proxy reencryption (PRE), InterPlanetary File System (IPFS), and trusted execution environment (TEE) for managing a fully decentralized and robust system. We implement a proof-of-concept system in an Ethereum-based environment, which is used for testing and vulnerability checks. We present the cost and security analyses and discuss the generalization aspect of the solution. Our smart contracts and testing scripts are publicly available under an open-source license.

Hybrid Encryption Scheme for Hospital Financial Data Based on Noekeon Algorithm

The previous encryption methods of hospital financial data have the problem of overburden. Therefore, a research study on hybrid encryption of hospital financial data based on Noekeon algorithm is proposed. From the basic principles of the Noekeon algorithm and the application and implementation of the Noekeon algorithm, a hybrid encryption scheme for hospital financial data based on the Noekeon algorithm is designed. In order to improve the security of the encryption system, the RSA algorithm is used to encrypt the encrypted content twice. The hybrid algorithm realizes the hybrid encryption of the hospital's financial data. Finally, a hybrid encryption system for hospital financial data based on Noekeon algorithm is designed. Experimental results show that this method has a higher success rate and better comprehensive performance. It not only improves the encryption efficiency of hospital financial data but also enhances the security of hospital financial data, which has greater application value.