User's Private Information Research Articles

Understanding the adoption of digital conferencing tools: Unpacking the impact of privacy concerns and incident response efficacy

Since working from home has become a new norm for many individuals worldwide, digital-conferencing tools have increased in popularity. To date, few studies have investigated how the adoption of digital-conferencing tools relates to user information privacy concerns against security incidents and coping appraisal. This paper utilizes an integrated IUIPC and Privacy Calculus model to explore the impact of privacy concerns, incident response efficacy, trust, and risk involved in digital conferencing adoption. To test the proposed model, we administer a survey to 212 participants and find that perceived benefits, trusting beliefs, and personal Internet interest increase intention to use digital conferencing software while risk beliefs decrease behavioral intentions. In addition, we find incident response efficacy to be a salient construct in explaining users’ trusting beliefs. The study provides recommendations for digital conferencing service providers on how to improve consumer adoption through strengthened security practices and incident response strategies.

Integrated demand-side management for multi-energy system based on non-cooperative game and multi-energy pricing

This paper proposes an integrated demand-side management (IDSM) approach based on non-cooperative game and multi-energy pricing strategy for a regional integrated energy system (RIES). On the demand side, the competition among multi-energy users was modeled as a non-cooperative energy consumption game, and the conditions to ensure the existence and uniqueness of the Nash equilibrium of the non-cooperative game were given. Considering the privacy of user information, a distributed multi energy consumption control algorithm based on dual theory and gradient descent method was designed to search for the optimal multi-energy consumption and the multi-energy daily retail price. In addition, for the energy supply side, the scheduling strategy of energy hub (EH) was proposed based on linear programming (LP) theory to optimize equipment operation. The results show that this approach can enable users and EH to coordinate optimization, so as to reduce the peak-to-average ratio (PAR) and energy cost of the system, and ensure the balance between supply and demand of multi-energy.

A robust VC scheme for securing cloud data storage

Visual Cryptography (VC) was created to encode images into several shares and decode them by aggregating the shares without the use of costly standard cryptanalysis. To enhance the quality and security of visual images in cloud storage, a Robust Collusion Avoidance-based Secure Signific VC (RCA-SSVC) scheme was designed, which creates the collusion-resistance Secret Image (SI) shares and embeds them into the Cover Image (CI) to prevent collusion threats. But it takes a long-time during access for users to recover the real image whenever receiving fake shares kept in the cloud by unauthorized users. Therefore, in this article, a new 2-level data protection scheme is proposed using QR code and RCA-SSVC. In this scheme, the public and private information of users are taken as QR code, which is encoded into sub-pixel blocks of all shares during the share creation phase. Also, a random permutation is applied to the created shares to disrupt the sub-pixels in a single block, which can avoid attackers from guessing the location of the Secret QR (SQR) code’s pixels. During the image restoration step, the restoration process is initiated once the user identity is authenticated by reading QR code information, which is matched with the QR code embedded in the shares. So, image restoration using illegitimate shares during access is prevented. At last, the test outcomes reveal that the RCA-SSVC-QR establishes maximum confidentiality in image access from the cloud storage compared to the classical VC schemes.

A Robust and Effective Two-Factor Authentication (2FA) Protocol Based on ECC for Mobile Computing

The rapid development of mobile computing (e.g., mobile health, mobile payments, and smart homes) has brought great convenience to our lives. It is well-known that the security and privacy of user information from these applications and services is critical. Without the prevention provided by an authentication mechanism, safety vulnerabilities may accumulate, such as illegal intrusion access resulting in data leakage and fraudulent abuse. Luckily, the two-factor authentication (2FA) protocols can secure access and communication for mobile computing. As we understand it, existing 2FA authentication protocols weaken security in the pursuit of high efficiency. How efficiency can be achieved while preserving the protocol’s security remains a challenge. In this study, we designed a robust and effective 2FA protocol based on elliptic curve cryptography (ECC) for authentication of users and service providers. We proved the robustness (respectively, the effectiveness) of the presented protocol with the heuristic analysis and security verification provided by the ProVerif tool (respectively, with a performance comparison based on six schemes). Performance comparisons in terms of message rounds, communication, and computation overheads showed that our scheme was superior to the exiting schemes or comparable as a whole; i.e., only two rounds, 1376 bits, and 1.818 ms were required in our scheme, respectively. The evaluation results showed that the proposed 2FA protocol provides a better balance between security and availability compared to state-of-the-art protocols.

CCM-FL: Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private information of users in federated learning has become an important research topic. Compared with the differential privacy (DP) technique and secure multiparty computation (SMC) strategy, the covert communication mechanism in federated learning is more efficient and energy-saving in training the machine learning models. In this paper, we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks. Different from the previous works about covert communication in federated learning, most of which are considered in a centralized framework and experimental-based, we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents, the time complexity of which is O(log n), approximating to the optimal solution. Secondly, for the federated learning without parameter server, which is a harder case, we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log log Δ log n) times, approximating to the optimal solution. Δ is the maximum distance between any pair of learning agents. Theoretical analysis and numerical simulations are presented to show the performance of our covert communication mechanisms. We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

SEMDA: Secure and Efficient Multidimensional Data Aggregation in Smart Grid without a Trusted Third Party

Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user’s electricity consumption or user’s main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user’s multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users’ privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption.

LEGAL CONSEQUENCES OF ELECTRONIC AGREEMENTS REVIEWED FROM ARTICLE 1866 OF THE CIVIL CODE

Legal issues with regard to authenticity, authenticity, and proof arise frequently because no laws exist to control the private information of users of electronic agreements. The aim of this research is to determine whether or not there are issues with the legal binding force of agreements established via electronic means. This study employs a normative qualitative approach, based on the analysis of secondary data and bolstered by original data collected in the field. The findings prove that digital investigative tools can be used to verify the legitimacy, veracity, and integrity of electronic contracts. A person's permission is required before any of their personally identifiable information (PHI) can be used in any way, shape, or form via technological media. The evidentiary weight of an electronic or digitally signed deal is the same as that of a handwritten one. As progress is made toward open proof, the judicial system can make use of the system. Given the prevalence of online media in modern business dealings, it follows that any evidence acquired from any source, provided it is true, is admissible so long as it does not violate public order.

Incentive‐based demand response program with self‐reported baseline supported by blockchain technology

AbstractThis research presents a decentralised incentive‐based demand response (DR) program using blockchain technology. Consumers self‐report baseline to the system operator (SO), the smart contract confirms the validity of the data to execute transactions and finally the validators record the information on the blockchain network. During the DR event, a set of consumers are randomly selected to deliver the required load reduction. The signalled consumer who delivers the load reduction is rewarded, and the non‐called consumers who diverge from their reported baseline are penalised. The randomness of choosing the consumers and penalty function restrict the baseline inflation. Here, we create a blockchain network and deploy a smart contract on the Ethereum build platform. A DR event scenario is adopted with residential houses data sets, all consumers report their baseline information to the SO through the Internet and smart meter. The SO calls four users to deliver the essential load reduction according to the probability of choosing a consumer. The smart contract verifies the received information to start transaction execution. We use proof of authority mechanism to select validation nodes from the participants using voting system. They validate each block before adding it to the blockchain. Last, the monetary transactions settle in participants' wallets. Our results confirm that decentralised systems like blockchain can significantly improve transparency, openness, and customer participation in the DR program. Also contributes to the security and privacy of user information with a minimal investment in new infrastructure.

Security and privacy research of Internet of things systems in mobile networks

The escalating integration of Internet of Things (IoT) devices with mobile networks presents unprecedented opportunities and challenges in the context of security and privacy. This paper offers a comprehensive overview of recent research focusing on the security and privacy concerns associated with IoT systems operating within mobile networks. The fundamental concepts, emerging threats, and potential solutions in this dynamic landscape are examined. The distinctive characteristics of IoT devices and their unique security and privacy requirements are outlined. The paper discusses inherent vulnerabilities, including limited computational resources, device heterogeneity, and the expansive attack surface resulting from interconnected devices. Additionally, the growing risks associated with data collection, transmission, and storage within mobile network environments are explored, emphasizing the importance of protecting user privacy and sensitive information. State-of-the-art security mechanisms and protocols designed to safeguard IoT systems in mobile networks are surveyed. Approaches such as encryption, authentication, access control, and intrusion detection systems are discussed as means to mitigate potential threats. The paper addresses the challenge of secure device onboarding and underscores the importance of ensuring the integrity and authenticity of IoT components within the network. Furthermore, the role of standards and regulatory frameworks in shaping security and privacy requirements for IoT systems is examined. Initiatives by organizations such as the Internet Engineering Task Force (IETF) and the European Union’s General Data Protection Regulation (GDPR) are discussed in relation to their influence on the development and deployment of secure IoT solutions. In conclusion, this review highlights the ongoing need for continuous research and development in the security and privacy domains of IoT systems in mobile networks. As the adoption of IoT devices and mobile network technologies continues to grow, maintaining vigilance in addressing evolving threats and ensuring compliance with emerging regulations is imperative. This paper aims to provide valuable insights into the evolving landscape of IoT security and privacy, catering to researchers, practitioners, and policymakers, and paving the way for a more secure and privacy-preserving future for IoT deployments in mobile networks.

Behavior Privacy Preserving in RF Sensing

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this article, we propose a privacy-preserving deep neural network named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.

A Secure and Efficient Access Control Scheme for Shared IoT Devices over Blockchain

The concept of shared IoT devices has attracted much attention from the industry sector, academia, and financial institutions, providing various benefits, such as saving resources, reducing personal expenses, and providing convenience. Although shared IoT devices facilitate people’s lives and work, the information exchange is over wireless networks that may suffer from some security attacks such as unauthorized access to a shared device or some private information of legitimate users being leaked. It makes the secure access control to the shared IoT devices become an intractable issue. In order to guarantee the access right of the legitimate users, to prevent the problems of privacy leakage and unnecessary economic disputes, a secure decentralized access control scheme for shared IoT devices is proposed leveraging the technologies of blockchain and a proposed authentication protocol in this paper. The new lightweight authentication protocol is proposed to perform mutual authentication between the user and the IoT device. To protect the privacy of the user, the instruction data are encrypted by a temporary session key negotiated between the user and the IoT device with the help of blockchain which enables nontamperable transactions and prevents central corruption and single point of failure. In our scheme, blockchain is maintained by the gateway nodes an acts as a distributed database and a smart contract for shared service is deployed on it. The smart contract has three functions in our scheme: (1) achieving the prepayment of users and settlement for the service contributor, (2) participating in a verification step during the key negotiation to prevent some malicious behaviour from users or devices, (3) recording the workload of the gateway. Finally, a comprehensive analysis on the safety and reliability of the entire scheme is carried out; extensive simulation experiments are conducted to reveal the authentication protocol is efficient and the scheme is feasible.

Defense against membership inference attack in graph neural networks through graph perturbation.

Graph neural networks have demonstrated remarkable performance in learning node or graph representations for various graph-related tasks. However, learning with graph data or its embedded representations may induce privacy issues when the node representations contain sensitive or private user information. Although many machine learning models or techniques have been proposed for privacy preservation of traditional non-graph structured data, there is limited work to address graph privacy concerns. In this paper, we investigate the privacy problem of embedding representations of nodes, in which an adversary can infer the user's privacy by designing an inference attack algorithm. To address this problem, we develop a defense algorithm against white-box membership inference attacks, based on perturbation injection on the graph. In particular, we employ a graph reconstruction model and inject a certain size of noise into the intermediate output of the model, i.e., the latent representations of the nodes. The experimental results obtained on real-world datasets, along with reasonable usability and privacy metrics, demonstrate that our proposed approach can effectively resist membership inference attacks. Meanwhile, based on our method, the trade-off between usability and privacy brought by defense measures can be observed intuitively, which provides a reference for subsequent research in the field of graph privacy protection.

Privacy‐preserving enhanced dummy‐generation technique for location‐based services

SummaryLocation‐based services (LBS) has become an intrinsic part of our everyday life. However, the flexibility and convenience provided by LBS are at the cost of user privacy since untrusted LBS server can leak private information of users. To overcome the privacy issues observed in LBS, a novel dummy‐generation based privacy preservation technique is proposed in this article. The proposed dummy‐generation technique is a circle‐based technique which generates dummy locations in the circle area and is effective against center‐of‐anonymized spatial region attack, map‐matching attack, and location‐homogeneity attack. Additionally, an edge computing enabled framework is proposed, which can be used in the IoT environment. The edge computing enabled framework helps in handling the resource poverty issues of the service requesting devices and provides the low latency solution. The security analysis of our proposed dummy‐generation technique reflects that the proposed technique is resilient to specific attacks for different adversary attack models. The results obtained through simulations suggest that our technique performs better than the pre‐existing techniques.

A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.

Research on Information Leakage Tracking Algorithms in Online Social Networks

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm.

Blockchain-Enabled Conditional Decentralized Vehicular Crowdsensing System

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

"Using MongoDB to Understand the Underlying Methods Techniques Encryption in NoSQL database"

There is a need for appropriate encryption methods, which must be adhered by the concerned parties, in order to provide a high level of security for the secret data. In this study, an examination of the different encryption algorithms and their performance in the management of private data with authentication, access control, secure configuration, and data encryption is presented. It includes enhancing MongoDB's level-based access protected model and adding privacy keys for security and monitoring purposes respectively. The NoSQL data stores, also known as highly compressed data on non-relational database management systems, which provide support for data management of internet user programs, do not now offer this service. Data security, encryption techniques, MongoDB, and NoSQL are some of the keywords that come to mind.The proper encryption methods used to various crucial data fields provide data protection without adversely affecting the database's performance in terms of speed or memory usage. Due to the use of appropriate encryption techniques, this is achievable. Since document-oriented and unstructured data are generally kept in databases, it is frequently used to handle various sorts of data. Examples of such databases include MongoDB, Cassandra, CouchDB, Redis, Hyper-table, and others. Since they are open source, there is a critical necessity to offer good security and protect the user's private information when it is in transit or at rest. There is a need for a single solution that can improve the security of data transfer by providing an improved encryption method that speeds up processes and uses less memory when maintaining databases.

"Evaluation of NoSQL Database MongoDB with Respect to JSON Format Data Representation "

The current information technology advancement is attributed to the development of more data-intensive applications. However, due to stringent limitations on data structure, data relations, and other factors, relational databases do not allow us to work with huge data sets or maintain high volume databases. Unstructured data from many industries, including a variety of forms, must be processed and stored in databases. Thus, NoSQL types offer solutions to a number of problems associated with huge data bases. Largely because of the benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many No SQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc. A document-oriented, cross-platform database called MongoDB offers excellent performance, high availability, and simple scalability. The MongoDB Query Language (MQL), created for ease of use by developers, is used by MongoDB. The documentation contrasts the syntax of MQL versus SQL for typical database operations. When your data is document-centric and doesn't fit well into the schema of a relational database, when you need to support vast size, when you need to quickly prototype, and for a few additional use cases, NoSQL databases like MongoDB are a solid option.The proper encryption methods used to various crucial data fields provide data protection without adversely affecting the database's performance in terms of speed or memory usage. Due to the use of appropriate encryption techniques, this is achievable. Since document-oriented and unstructured data are generally kept in databases, it is frequently used to handle various sorts of data. Examples of such databases include MongoDB, Cassandra, CouchDB, Redis, Hyper-table, and others. Since they are open source, there is a critical necessity to offer good security and protect the user's private information when it is in transit or at rest. There is a need for a single solution that can improve the security of data transfer by providing an improved encryption method that speeds up processes and uses less memory when maintaining databases.

SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things

In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge.As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant’s resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant’s computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme’s security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme’s universality.

Naive Bayes Algorithm Mining Mobile Phone Trojan Crime Clues

After the mobile phone virus infects the mobile phone, it can transmit the real-time information of the user to the designated place set by the virus through the built-in recorder and camera on the mobile phone, thereby causing information leakage. With the rapid development of the Internet, the penetration rate of mobile terminals is also increasing day by day. As an emerging mobile terminal, smart phones have now fully occupied the market. With this trend, the importance of mobile phone information security is also increasing day by day. How to prevent mobile phone virus has gradually become an important issue. Trojan horse crime cases have different manifestations and behavioral characteristics from traditional cases. They have the characteristics of low crime cost, high income, high concealment, novel criminal methods, and great difficulty in detection, which brings greater difficulties to the public security organs in their investigation and detection. And the current research on mobile phone virus behavior is still in the preliminary stage, and some existing detection models can only target random networks. Trojan horses, viruses, and malicious software for smartphones have sprung up like mushrooms after rain, seriously infringing on the data security of mobile communication terminals, such as mobile phones and causing incalculable losses to users. This paper proposes a naive Bayesian algorithm to mine the clues of the criminal cases of mobile phone Trojans. It helps detect and discover new viruses at the beginning of an attack, allowing them to be more effectively defended and contained. And based on the feature set data extracted from the network data packets, it conducts an in-depth analysis of the current business behaviors of mobile phone Trojans, such as propagation and implantation, remote control, leakage of user privacy information, and malicious ordering, and extracts its behavior characteristics. Thus, unknown mobile Trojan horses that are taking place can be detected. The experimental results of the naive Bayesian classification algorithm proposed in this paper show that the algorithm improves the accuracy of mobile phone Trojan virus mining by 28%, which plays a significant role.