The initiatives for redecentralization of the Web such as SoLiD aim to enhance users’ privacy by enforcing transparency about the data used by Web applications. However, it is a challenge for a Web application acquiring data from third-party sources to trust data originating from many or even hidden parties. A decentralized web application requires to evaluate trust and take trust-aware decisions autonomously without relying on a centralized infrastructure. While many related trust models consider direct or reputation-based trust for making trust-aware decisions, in decentralized web applications content and context factors (called content trust) become critical due to the arbitrary number of potential data providers and the contextual nature of trust. Besides, the dynamic nature of the decentralized web necessitates trust-aware decisions that are made autonomously by the machine in a collaborative environment without further human intervention. To address these challenges, we present ConTED, a content trust evaluation framework for enabling decentralized Web applications to evaluate content trust autonomously. We also describe the architecture concept, which makes it feasible to integrate content trust models for decentralized Web applications. To demonstrate the feasibility, ConTED is integrated with aTLAS testbed, a web-based test bed to examine trust for a redecentralized web. Finally, we evaluate ConTED in terms of scalability and accuracy through a set of experiments.

Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacypreserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable.

Face verification is by far the most popular biometrics technology used for authentication since it is noninvasive and does not require the assistance of the user. In contrast, fingerprint and iris identification technologies require the help of a user during the identification process. Now the technology behind facial recognition has been around for years but recently as its grown more sophisticated is applications have expanded greatly. These days a third-party service provider is often hired to perform facial recognition. The sensitivity of face data raises important privacy concerns about outsourcing servers. In order to protect the privacy of users, this paper discusses privacy-preserving face recognition frameworks applied to different networks. In this survey, we focused primarily on the accuracy of face recognition, computation time, and algorithmic approaches to face recognition on edge and cloud-based networks.

Fraud detection is an integral part of financial security monitoring tool; however, the traditional fraud detection method cannot detect the existing malicious fraud, and the clouds will produce data revealing that the risk of fraud detection system can not protect the privacy of detected object, so the fraud detection data privacy security becomes a significant problem,Homomorphic encryption as a demonstrable cryptography cloud privacy computing outsourcing scheme can ensure that cloud computing can perform ciphertext polynomial calculation under the dense state data without direct contact with the accurate data of users, so as to ensure data privacy security. Aiming at the data privacy security problems in the process of fraud detection, this paper combined homomorphic encryption and Logistic regression fraud detection technology to study the Logistic regression fraud detection algorithm under homomorphic ciphertext and constructed a cloud privacy fraud detection method based on customer service and cloud computing services. CKKS encryption scheme is used to encrypt the fraud data set and realize the Logistic regression fraud detection algorithm under ciphertext. The experiment proves that the difference between the fraud detection accuracy on ciphertext and plaintext is less than 3\%. Under the condition of ensuring the privacy of sensitive data to be detected, the effect of the fraud detection model is not affected.

The problem of privacy protection of trajectory data has received increasing attention in recent years with the significant grow in the volume of users that contribute trajectory data with rich user information. This creates serious privacy concerns as exposing an individual’s privacy information may result in attacks threatening the user’s safety. In this demonstration we present T P 3 a novel practical framework for supporting trajectory privacy preservation in Mobile Cloud Environments (MCEs). In T P 3, non-expert users submit their trajectories and the system is responsible to determine their privacy exposure before sharing them to data analysts in return for various benefits, e.g. better recommendations. T P 3 makes a number of contributions: (a) It evaluates the privacy exposure of the users utilizing various privacy operations, (b) it is latencyefficient as it implements the privacy operations as serverless functions which can scale automatically to serve an increasing number of users with low latency, and (c) it is practical and cost-efficient as it exploits the serverless model to adapt to the demands of the users with low operational costs for the service provider. Finally, T P 3’s Web-UI provides insights to the service provider regarding the performance and the respective revenue from the service usage, while enabling the user to submit the trajectories with recommended preferences of privacy.

With the help of Voice-controlled Digital Assistants (VCDAs), end users can perform various tasks, such as creating shopping lists, setting reminders, or controlling smart home devices via voice commands. However, in multi-user environments, the different end users of VCDAs may not have access to the same controls to protect their privacy. The primary end users who set up VCDAs usually have full control over the data collected by VCDAs, including text transcripts and audio recordings of the other end users. In order for these secondary end users to gain access to privacy settings, they must also create an account with the appropriate manufacturer and accept an invitation from the primary end user to join the respective VCDA. As a result, they depend on the primary end user and the creation of a user account to be able to protect their privacy. Through a user account, however, personal information, such as name, address, or age can be linked to audio recordings, that poses additional privacy risks to secondary end users. For both primary and secondary end users, audio recordings are still maintained on cloud servers operated by manufacturers, resulting in a lack of transparency for all end users. In this paper, we thus propose an approach to improve the protection of both primary and secondary end users that reaches from the device set-up to its utilization. Our approach is based on the concept of a local registration and offline storage of voice commands.

A large number of establishments and organizations implement clouds to store their databases. More active attacks are used on clouds to get unauthorized access or to do harmful actions that may affect on user’s privacy. Therefore, many studies have proposed to increase the level of security in the clouds depended on several strategies. The behavior is one of the promising strategies that might prevent unauthorized or processes. In this paper, a set of features, are from several previous studies, and these features are based on the user activity and events in a special purpose cloud by which unauthorized process can be prevented and alert user about bad actions during his/her work in the cloud environment. The results of comparison show that the event-based features require less resources and time. Thus, they need to be enhanced by adding more informative features, or some available features form other strategies.

Cloud computing performs a significant part in sharing resources and data with other devices via data outsourcing. The data collaboration services, as a potential service given by the cloud service provider (CSP), is to assist the consistency and availability of the shared data amongst users. At the time of sharing resources, it is a complicated process for providing secure writing and access control operations. This study develops a Privacy Preserving Encryption with Optimal Key Generation Technique (PPE-OKGT) for CC environment. The presented PPE-OKGT technique secures the data prior to storing in the cloud sever via encryption process. For accomplishing this, the presented PPE-OKGT technique employs data encryption technology to secure the input data into a hidden format. Besides, in order to improve secrecy, the presented PPE-OKGT technique designs a chaotic search and rescue optimization (CSRO) algorithm for optimal generation of keys. The promising performance of the PPE-OKGT technique can be verified using a set of experimentations. A comprehensive comparison study reported the enhancements of the PPE-OKGT technique over other models.

Cloud computing platforms are the widely used state of the art platforms by various organizations. Data storage and data sharing services are the most widely used services in the cloud, while the maintenance of data integrity is a big challenge. A public cloud platform which is not reliable, users must generate digital signature of their data and then share generated signature for integrity auditing. Any attack on cloud can compromise the users valuable data which is most likely carried out by external entity. By generating signature we can write the rules of who can access update or delete the data. If data is updated by unauthorized user, then auditing can identify which data is compromised. Here we are using asymmetric keys that is when user upload his/her data over cloud then digital signature will get created with users private key, and when TPA (Third Party Auditor) wants to check the integrity of that users data then he can generate digital signature with users public key. Not only carried out low cost for data storage by compressing data but also established data access protocol to maintain data privacy.

When an underwater acoustic sensor network (UASN) is applied to underwater data collection, different data importance rating (DIR) of sensor nodes will affect the scheduling time slot of data collection. In this paper, we propose a Q-learning and DIRbased media access control (Q-DIR MAC) protocol for dynamic clustering underwater acoustic sensor networks (UASNs), in which the nodes in the network may drift with the movement of ocean currents. We use k-mean algorithm to divide the nodes into several clusters. Each partitioned cluster is composed of one cluster head (CH) and several cluster members (CMs). The CMs can be divided into three levels according to the DIR: non-urgent, normal, and very urgent. The number of three types of nodes follows normal distribution. The data importance of each node is introduced into reward function design of Q-learning. The results show that, in the dynamic clustering UASNs, the proposed QDIR MAC protocol can ensure that important data can be sent to the destination node in time without reducing the data success rate under the condition of priority transmission mechanism.

Propagation delay and channel loss are two vital factors affecting reliability of Underwater Acoustic Networks (UANs). Different from land networks, UANs have long propagation delay and poor channel quality, which lead to serious data collision and high bit error rate, respectively. However, complex underwater environments impose great challenges to evaluate propagation delay and channel loss. As temperature is the most critical factor affecting them, in this paper, we propose to employ temperature to evaluate them. However, existing temperature prediction research are insufficient for accuracy or efficiency. This paper proposes a temperature prediction-assisted approach for evaluating propagation delay and channel loss, aiming to improve reliability and performance of underwater acoustic networks. We build a nonlinear autoregressive dynamic neural network-based temperature prediction model to improve prediction accuracy and reduce time complexity. Then, we evaluate propagation delay and channel loss considering different marine environments, including shallow and deep sea. Extensive simulation results show that our approach performs better than five advanced baselines.

With the rapid development of underwater sensor networks, the design of underwater demodulators become increasingly significant. However, underwater acoustic communication is faced with many problems such as propagation time delay, multipath effect and Doppler effect due to the complexity of underwater environment. Demodulation of underwater communication signals is a challenging task. To solve this problem, we propose a novel binary phase shift keying (BPSK) demodulator for underwater acoustic communication based on convolutional neural network, which demodulates the modulation data by detecting the position of phase shift. The method proposed in this paper significantly reduces the bit error rate (BER) compared with the results of the traditional method in URPC1 datasets (Underwater Robot Picking Contest).

Underwater acoustic sensor network (UASN) is a promising underwater networking technology for wide applications, but there is an urgent need to design reliable and low power consumption routing protocols for UASN to extend network lifetime due to the limited energy supply. In this paper, we propose a Q-learning and data priority-based routing protocol with dynamic computing cluster head (QD-DCR) to extend the network lifetime of UASN. In QD-DCR protocol, the underwater nodes are clustered and set the cluster head (CH) nodes, which are only responsible for computing the optimal path of data transmission and the storage of Q-value table, while the non-CH nodes are responsible for data transmission. Meanwhile, according to the data priority, we design different data transmission methods that can effectively use the limited resources of UASN to transmit urgent data. To further make the residual energy of sensor nodes evenly distributed, we also design the dynamic selection of CH node, which can avoid the potential energy holes. In addition, we adopt Q-learning to determine the optimal next hop instead of the greedy next hop in a cluster. We also define an action utility function that takes into account both residual energy and node depth to extend the network lifetime by distributing the residual energy evenly. Simulation results show that the proposed QD-DCR protocol can effectively extend the network lifetime compared with a classic lifetime-extended routing protocol (QELAR), while alleviating the issue of uneven distribution of the residual energy in the network.

Underwater Acoustic Sensor Networks (UASNs) is a prominent field in communications due to several applications. UASNs enable underwater data collection and monitoring in different applications. UASNs face several challenges like node mobility, low bandwidth, high energy consumption, and routing. The complexity of the underwater routing is increased due to node mobility. Several underwater routing protocols exist in the literature; they determine next-hop based on different criteria. Some criteria to select next-hop are link quality, residual energy, hop-count, etc. Many underwater routing protocols either use hop-count or hop-count as one of the criteria to choose nexthop. Such routing protocols result in lower hop-count, resulting in smaller end-to-end delays. These routing protocols are instrumental in the delay-sensitive applications where the endto-end delay is the primary requirement. However, maintaining up-to-date information of the hop-count of nodes is one of the major challenges due to frequent changes in underwater topology caused due to the water current. This survey paper focuses on underwater routing protocols based on hop-count in selecting the next-hop. It focuses on updating hop-count information in various hop-count-based underwater routing protocols.

Traditional Web application category recognition is implemented by fingerprint rule matching, which is difficult to extract fingerprint rules and has limited coverage. At present, many improved identification methods semi-automatically extract fingerprints through certain rules and identify Web application categories through clustering or classification algorithms, but still rely on fingerprint rules and human intervention, and the time complexity of classification is too high to process a large amount of data. This paper proposes Multi-layer Simhash Algorithm and combines DBSCAN clustering to realize intelligent identification of Web application types, pioneering the complete automation of fingerprint identification of Web applications. This method has the function of discovering unknown Web applications and predicting unknown application types, and solves the problems of fingerprint rule extraction and manual dependence of Web applications. This paper through the TF-IDF algorithm to extract the Web page text key words and weight, Then, Multi-layer Simhash Algorithm is used to transform text feature words and weights into binary characteristic hash value, at last, the hamming distance between the input Web page and the characteristic hash value of the known category is compared with the radius of the base class, which determines the category of the input Web application. The experimental results show that the accuracy of Web application category recognition and prediction is more than 97\% and 93\% respectively.

Providing security to the IoT system is very essential to protect them from various attacks. Such security features include credential management to avoid hard-coding of credentials in web applications, key management for secure inter-device communication and assignment of trust score to the devices based on various parameters. This work contains the design and implementation details of an open source simulation environment with credential management, key management and trust score calculation features. In credential management, credentials are sent to the target device which is then stored in a JSON file. Web application in the device makes use of these credentials for authentication. In key management, X.509 certificate and private key file are generated. They are used for secure message communication using a session key that is secretly exchanged between the devices. For trust score calculation, parameters are collected from the device. Feedback parameters given by other devices are also sent to the centralised server. The dynamic weighted average model is applied to the trust values derived from these parameters to get the trust score of the device. In addition to the design, the source code of our simulation environment is also made publicly available so that researchers can alter and extend its capabilities.

Web technologies have created a worldwide web of problems and cyber risks for individuals and organizations. In this paper, we evaluate web technologies and present the different technologies and their positive impacts on individuals and business sectors. Also, we present a cyber-criminals metrics engine for attack determination on web technologies platforms’ weaknesses. Finally, this paper offers a cautionary note to protect Small and Medium Businesses (SMBs) and make recommendations to help minimize cyber risks and save individuals and organizations from cyberattack distress.

With the advancement in computing power and speed, the Internet is being transformed from screen-based information to immersive and extremely low latency communication environments in web 3.0 and the Metaverse. With the emergence of the Metaverse technology, more stringent demands are required in terms of connectivity such as secure access and data privacy. Future technologies such as 6G, Blockchain, and Artificial Intelligence (AI) can mitigate some of these challenges. The Metaverse is now on the verge where security and privacy concerns are crucial for the successful adaptation of such disruptive technology. The Metaverse and web 3.0 are to be decentralized, anonymous, and interoperable. Metaverse is the virtual world of Digital Twins and nonfungible tokens (NFTs).The control and possession of users’ data on centralized servers are the cause of numerous security and privacy concerns.This paper proposes a solution for the security and interoperability challenges using Self-Sovereign Identity (SSI) integrated with blockchain. The philosophy of Self-Sovereign Identity, where the users are the only holders and owners of their identity, comes in handy to solve the questions of decentralization, trust, and interoperability in the Metaverse. This work also discusses the vision of a single, open standard, trustworthy, and interoperable Metaverse with initial design and implementation of SSI concepts.

The internet has made everything convenient. Through the world wide web it has almost single-handily transformed the way we live our lives. In doing so, we have become so fuelled by cravings for fast and cheap web connections that we find it difficult to take in the bigger picture. It is widely documented that we need a safer and more trusting internet, but few know or agree on what this actually means. This paper introduces a new body of research that explores whether there needs to be a fundamental shift in how we design and deliver these online spaces. In detail, the authors suggest the need for an internet security aesthetic that opens up the internet (from end to end) to fully support the people that are using it. Going forward, this research highlights that social trust needs to be a key concern in defining the future value of the internet.

Current and future networks must tackle identity management to authenticate and authorise users to access services. Identity management solutions are widely employed nowadays, where one authenticates in third-party services using account information stored securely in identity providers. Solutions like OpenID Connect relying on OAuth 2.0 are employed to support Single-Sign-On, facilitating users’ login process, which does not need to manage multiple accounts in several services. Despite their wide usage in several domains (enterprise, web applications), they only consider entities like persons. Thus, trust information regarding the levels of trust a person can perceive when accessing services with its devices in specific environments (e.g. untrusted networks like public hotspots) can be employed to protect access to data. OIDC-TCI is an approach to convey context information reflecting the trust relations between endusers, the applications/services running in devices, and a specific environment where access to sensitive resources needs to be authorised. The results demonstrate OIDC-TCI as a feasible solution to convey trust with minimal impact, in compliance with OpenID Connect, in a web service - TeaStore.

COVID-19 has taught us the need of practicing social distancing. In the year 2020 because of sudden lockdown across the globe, E-commerce websites and e-shopping were the only escape to fulfill our basic needs and with the advancement of technology putting your websites online has become a necessity. Be it food, groceries, or our favorite outfit, all these things are now available online. It was noticed during the lockdown period that the businesses that had no social presence suffered heavy losses. On the other hand, people who had established their presence on the internet saw a sudden boom in their overall sales. This project discusses how the recent advancement in the field of Machine Learning and Artificial Intelligence has led to an increase in the sales of various businesses. The machine learning model analyses the pattern of customer’s behavior which affects the sales builds a dataset after many observations and finally helps generate an algorithm which is an efficient recommendation system. This project also discusses how cyber security helps us have secured and authenticated transactions which have aided ecommerce business growth by building customer s trust.

We are adopting blockchain-based security features for the usage in web service applications \& platforms. These technology concepts allow us to enhance the level of trustworthiness for any kind of public web service platform. Related platforms are using simple user registration and validation procedures, which provide huge potential for illegal activities. In contrast, more secure live video identity checks are binding massive resources for the individual, staff-intensive validation tasks. Our approach combines traditional web-based service platform features with blockchain-based security enhancements. The concepts are used on two layers, for the user identification procedures as well as the entire process history on the web service platform.

To improve the security and reliability of remote terminals under trusted cloud platform, an identity authentication model based on DAA optimization is proposed. By introducing a trusted third-party CA, the scheme issues a cross domain DAA certificate to the trusted platform that needs cross domain authentication. Then, privacy CA isolation measures are taken to improve the security of the platform, so that the authentication scheme can be used for identity authentication when ordinary users log in to the host equipped with TPM chip. Finally, the trusted computing platform environment is established, and the performance load distribution and total performance load of each entity in the DAA protocol in the unit of machine cycle can be acquired through experimental analysis. The results show that the scheme can take into account the requirements of anonymity, time cost and cross domain authentication in the trusted cloud computing platform, and it is a useful supplement and extension to the existing theories of web service security.

The computing capability of the embedded systems and bandwidth of the home network increase rapidly due to the rapid development of information and communication technologies. Many home appliances such as TVs, refrigerators, or air conditioners are now connected to the internet, then, the controlling firmware modules are automatically updatable via the network. TR-069 is a widely adopted standard for automatic appliance management and firmware update. Maintaining a TR069 network usually involves the design and deployment of the overall security and trust infrastructure, the update file repository and the update audit mechanisms. Thus, maintaining a dedicated TR-069 network is a heavy burden for the vendors of home appliances. Blockchain is an emerging technology that provides a secure and trust infrastructure based on distributed consensus. This paper reports the results of our initial attempt to design a prototype of a multitenant TR-069 platform based on the blockchain. The core idea is to reify each automatic deployment task as a smart contract instance whose transactions are recorded in the append-only distributed ledger and verified by the peers. Also, the overall design should be transparent to the original TR069 entities. We have built a prototype based on the proposed architecture to verify the feasibility in three key scenarios. The experimental results show that the proposed approach is feasible and is able to scale linearly in proportion to the number of managed devices.

Connected vehicles need to generate, store, process, and exchange a multitude of information with their environment. Much of this information is privacy-critical and thus regulated by privacy laws like the GDPR for Europe. In this paper, we analyze and rate exemplary data (flows) of the electric driving domain with regard to their criticality based on a reference architecture. We classify the corresponding ECUs based on their processed privacy-critical data and propose technical mitigation measures and technologies in form of generic privacy-enhancing building blocks according to the classification and requirements derived from the GDPR.