Privacy leakage remains a critical challenge in online social networks (OSNs), where users continuously share information despite potential risks. The interconnected nature of OSNs makes the control of privacy information diffusion more complex. This paper proposes a novel adaptive privacy protection framework that can dynamically mitigate the privacy risks during privacy information diffusion. We model privacy diffusion and inference risks along propagation paths and develop a privacy policy generation mechanism using deep reinforcement learning (DRL) to optimize the trade-off between privacy protection and information sharing utility. Extensive experiments on real and synthetic social network datasets show that our method outperforms baseline approaches in controlling privacy risk while preserving the information sharing utility. Moreover, our study highlights the effectiveness of future reward prediction in reinforcement learning for privacypreserving decision-making.

ABSTRACT Background The rapid evolution of Internet of Things (IoT) technologies has driven innovations across domains such as robotics, autonomous systems, and environmental control. However, effectively learning graph‐based representations within these dynamic and heterogeneous systems remains a significant challenge, especially when scalability and adaptability are required. Aims This study aims to develop and evaluate a novel meta‐reinforcement learning (meta‐RL) framework that combines Deep Q‐Networks (DQNs) with Graph Convolutional Networks (GCNs) to learn adaptive and efficient representations of graph clusters. The primary objective is to enhance cluster‐based representation learning by integrating reinforcement learning with graph aggregation policies. Methods We propose a cluster policy‐GNN model that formulates optimal graph aggregation as a Markov Decision Process (MDP). The framework incorporates a cluster meta‐policy to guide node‐specific aggregation strategies and utilizes a combination of DQN and GCN for adaptive graph representation. Training involves clustering nodes based on policy‐determined hops and batching to ensure efficient GNN training. A custom reward function drives the reinforcement learning process to prioritize computational focus on the most informative subgraphs. Results Our experimental results, benchmarked on real‐world graph datasets, demonstrate that the proposed framework significantly outperforms existing state‐of‐the‐art methods, including static GNNs, alternating graph‐regularized networks, and causal‐aware neural architecture search models. The learned cluster policies effectively enhance representation learning by dynamically adjusting to the structural heterogeneity of input graphs. Improvements were observed across various domains and scales, validating the flexibility and generalizability of the method. Conclusion The proposed meta‐RL framework with integrated DQN and GCN modules offers a powerful and scalable approach for graph cluster representation learning. By introducing adaptive, node‐specific aggregation strategies guided by reinforcement learning, the method effectively captures complex graph structures and surpasses current techniques. Future work may explore real‐time adaptation and deployment in more dynamic IoT‐based applications.

Privacy inference poses a significant threat to users of online social networks (OSNs). To deal with this issue, a number of privacy-enhancing technologies have been proposed with the goal of achieving a balance between the protection of privacy and the utility of data. Previous studies, however, failed to take into consideration the impact of the interdependency of privacy (IoP), which dictates that privacy decisions made by some users may affect the privacy of some other users. The implication of IoP is that too much privacy may be disclosed when multiple individuals share data with the same data accessor because privacy conflicts resulting from independent privacy decisions would make it possible for adversaries to infer the privacy of the target user. Ideally, cooperation that preserves privacy should allow OSN users to respect each other’s privacy specifications so as to resolve such privacy conflicts caused by independent privacy decisions of individuals. To facilitate the design, we propose a privacy-preserving cooperation framework based on the evolutionary game theory to facilitate such cooperation. Based on the framework, the dynamics of user strategies regarding whether to participate in the cooperation are analyzed and an evolutionary stable state is derived to serve as the basis for incentivizing users to participate in cooperative privacy protection. Experiments based on real OSN data show that the proposed cooperation framework is effective in modeling the behaviors of users and that the proposed incentive allocation method can incentivize users to participate in the cooperation. The proposed cooperation framework can not only helps lower the threat to user privacy resulting from privacy inference by data accessors but also allows OSN service providers to design effective privacy protection policies.

Privacy protection is a hot topic in network security, many scholars are committed to evaluating privacy information disclosure by quantifying privacy, thereby protecting privacy and preventing telecommunications fraud. However, in the process of quantitative privacy, few people consider the reasoning relationship between privacy information, which leads to the underestimation of privacy disclosure and privacy disclosure caused by malicious reasoning. This paper completes an experiment on privacy information disclosure in the real world based on WordNet ontology .According to a privacy measurement algorithm, this experiment calculates the privacy disclosure of public figures in different fields, and conducts horizontal and vertical analysis to obtain different privacy disclosure characteristics. The experiment not only shows the situation of privacy disclosure, but also gives suggestions and method to reduce privacy disclosure.

Collaborative filtering based on single domains has become widely used in today's recommendation system. Nevertheless, it has two problems that need to be solved, i.e., the cold start problem and the data sparseness problem. As the result, cross-domain recommendation technology has emerged, which aims at integrating user preference characteristics from different domains. This paper proposes a collaborative filtering recommendation method based on multi-domain semantic fusion (CF-MDS). CF-MDS achieves cross-domain item similarity calculation through semantic analysis and ontology and integrates data from different domains iteratively based on domain relevance to rate users on target domain items and to produce a cross-domain user-item rating matrix. Collaborative filtering technology is then combined with multi-domain fusion recommendation algorithm. Experimental results show that the proposed method can deal effectively with the cold start problem and data sparsity problem that exist in traditional recommendation systems as well as can improve the diversity of recommendation. Compared to other cross-domain recommendation methods, the proposed method can better meet personal needs of users and also improve the accuracy of recommendation.

Internet of Things (IoT) can be used to promote many advanced applications by utilizing the sensed data collected from various settings. To reduce the energy consumption of IoT devices, and to extend the lifetime of network, the sensed data are usually compressed before their transmission through compressed sensing theory. By reconstructing the sensed data at the edge of network with more resourceful devices, such as laptops and servers, the intensive computation and energy consumption of the IoT nodes could be effectively offloaded. However, most of the existing data collection schemes are limited in their scalability, because the unified data reconstruction models of them are not suitable for large-scale surveillance scenarios. In our proposed scheme, the whole network is first partitioned into a number of data correlated clusters based on spatial correlation. Then, a data collection tree is built to collect the compressed data in a hybrid mode. Finally, the data reconstruction problem is modelled as a group sparse problem and solved through using an alternating direction method of multiplier-based algorithm. The performance of data communication and reconstruction of the proposed scheme is evaluated through experiments with real data set. The experimental results show that the proposed scheme can indeed lower the amount of data transmission, prolong the network life, and achieve a higher level of accuracy in data collection compared to existing data collection schemes.

Short Message Service (SMS) is a text messaging service component of smart phones, web, or mobile communication systems which requires a high level of security to provide user authentication and data confidentiality. To provide such security features, a high security communication protocol for SMS, called Message Security Communication Protocol (MSCP) was proposed. In this paper, MSCP is formally analyzed using an automated logic-based verification tool with attack detection capabilities. The performed formal verification reveals that the proposed protocol is susceptible to parallel session and denial-of-service (DoS) attacks. The reasoning why these attacks are possible is detailed and an amended protocol is proposed to counter the identified attacks. Formal verification of the amended protocol provides confidence regarding the correctness and effectiveness of the proposed modifications.

With the development of distributed technology, security problems are growing. Trust is considered an effective approach for enhancing security in distributed environment. By considering fuzziness and uncertainty of trust, we propose a trust quantification algorithm based on grey fuzzy comprehensive evaluation method. Simulation results show that the trust quantification method can reflect nodes' real trust situation, and a node's trust quantification value is in accord with its behavior.

The Internet and computers did not invent or even cause privacy issues. The issues existed long before the creation of computers and Internet. The existence of the Internet, computers and large data storage make it possible to collect, process and transmit large volumes of data, including personal data. In this paper, we shall study the privacy from following two different views, namely legal framework and computer security model, and attempt to identify the difference between them. Because of the difference, we further argue that the current computer security model is not sufficient to support the privacy requirements in the legal framework. We propose a computer model ¿privacy reference monitor¿ to handle those unsupported requirements. The design of the privacy reference monitor is privacy policy neutral with a small number of functions. With minimal functionalities, we believe that it is possible to implement a verifiable privacy reference monitor.

A wireless mesh network (WMN) presents a new wireless network technology for building commercial mobile ad hoc networks. However, security in WMN has not received enough attention in the research community. One main challenge in designing WMNs is the vulnerability of such networks to malicious attacks. In this paper, we propose a zone-based distributed key management scheme for WMNs in which we show that the proposed scheme would improve key management in security, expandability, validity, fault tolerance and usability.

A wireless mesh network (WMN) is a new wireless network technology and there is a trend to adopt the technology to build commercial mobile ad hoc networks. Since a WMN is such a network without fixed infrastructure and is operated in an open medium, any user within the range covered by radio wave may access the network. Therefore, a critical requirement for the security in WMN is the authentication of a new user who is trying to join the network. In this paper, we present a new authentication scheme based on a combination of techniques, such as zone-based hierarchical topology structure, virtual certification authority (CA), off-line CA, identity-based cryptosystem and multi-signature. We show that our scheme would improve authentication in security, computational overhead, traffic, authentication latency and storage space.

A mobile ad hoc network is a multi-path autonomous system comprised of many mobile nodes with wireless transmission capability. In this paper, we first review current research in intrusion detection in ad hoc networks. Then, by considering the characteristics of such networks in which free movement of mobile nodes can lead to frequent topological changes, especially network separation and convergence, we propose an intrusion detection scheme based on a combination of techniques, such as hierarchical topology structure, distributed voting, off-line CA and composite key management, and show that the proposed scheme could improve intrusion detection in the areas of security, expandability, validity, fault tolerance and usability.

As a fundamental requirement for providing security functionality in sensor networks, key management plays a central role in authentication and encryption. In this paper, we propose the adaptive key selection (AKS) scheme and the adaptive key selection algorithm for secure multiple deployment in sensor networks that target at providing high connectivity between different deployment sets of sensor nodes. Our simulation shows that the AKS scheme can greatly improve the connectivity of sensor nodes while maintaining the security of an existing multiple deployment scheme at the same time.

In component-based software engineering, software systems are mainly constructed based on reusable components, such as third-party components and in-house built components. Hence, system quality depends on the quality of the involved components. Any change of a component, it must be re-tested at the unit level, and re-integrated to form component-based application systems. Although a number of recently published papers address regression testing and maintenance of component-based systems, very few papers discuss how to identify component changes and impacts at the unit level, and find out the reusable test cases in a component's test suite to support its evolution. This paper focuses on component API-based changes and impacts, and proposes a systematic re-test method for software components based on a component API-based test model. The proposed method has been implemented in a component test tool, known as COMPTest. It can be used to automatically identify component-based API changes and impacts, as well as reusable test cases in a component test suite. The paper also reports this tool and its application results

Constructing component-based software using reusable components is becoming a widely used approach. Since the quality of a component-based system is highly dependent on the quality of its components, component quality validation becomes very critical to both component vendors and users. Effectively validating component quality needs adequate test models and testing coverage criteria. This paper proposes an adequate test model and test coverage criteria for component validation. The paper discusses a dynamic approach to analyze component test coverage based on the proposed test model and test coverage criteria. The major contribution of this paper is its dynamic test coverage analysis solution to monitor API-based component validation and reuse. The paper reports the recent development efforts of a component test coverage analysis tool, and presents an application example.

This paper addresses the detection of duplexity mismatch (DM) of media access devices in Ethernet networks. From the broad spectrum of sources we surveyed, including logging data from real networks, it appears that DM is surprisingly common and very severe. We show how DM introduces degenerative traffic anomalies capable of drastically reducing flow throughput. We, then, propose a novel detection algorithm based on end-to-end active probing. Our investigation is complemented by the implementation of a SW prototype. Extensive experimental evaluation is conducted in a real-world production LAN. The achieved results are encouraging and show that our prototype can be a very useful tool. Our evaluation attained a high success rate of misconfiguration detection: 99.72%. False positive and false negative rates are extremely contained: 0.00% and 0.28%, respectively. Therefore, our prototype appears to be a robust and reliable detection instrument from which network administrators and field engineers can benefit.

We summarize the implementation and experiment of the available bandwidth measurement algorithm (He et al. (2001)). Our experiment shows that, compared with other measurement techniques, this algorithm can achieve better performance, lower overhead and fast convergence. It can also self-adapt to any bandwidth and respond to resolution requirements. Therefore, no prior knowledge about bottleneck bandwidth is required and measurement resolution can be specified to meet application requirements, which is important for applications and services designed for high speed networks and multimedia applications due to the wide range of bandwidths that may be required.

We present a wide area network (WAN) load balancing architecture in this paper. This architecture provides a high degree of reliability, availability, flexibility and scalability. The scalability allows any number of load balancing servers to be deployed in a network. The reliability and availability allows the load balancing servers to be deployed anywhere in the network. The flexibility allows server selection to be applied to individual packets as well as to user sessions dynamically. In addition, this architecture supports a flexible way of selecting the load balancing servers to achieve desired performance. We also compare our architecture with some of the previous work to illustrate it advantages, effectiveness and practicality in fulfilling the requirements of WAN load balancing.

Given an information flow consisting of the flow path and the flow condition under which the flow takes place, the problem of determining whether the information flow is legal is considered; that is, whether the flow complies with the underlying nondiscretionary security policy of a trusted computing base (TCB). It is shown that the proposed approach to information-flow analysis has the advantage of eliminating the possibility of generating false illegal flow, namely flows that are identified by the analysis process to be illegal but which, in reality, are legal. Without eliminating false illegal flows from analysis, automated tools for secure information-flow analysis would be of limited use in this area because manual work would still be needed. Finally, it is shown how to apply this information-flow analysis approach to Secure XENIX and how information-flow analysis can help reduce the amount of effort for information-flow integration within TCB programs.>