Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Online social networks provide users with services such as online interaction, instant messaging, and information sharing. The friend search engine, a new type of social application, provides users with the service for querying the list of other individuals’ friends. Currently, the existing research focuses on independent attacks for friend search engines while ignoring the more complicated collusion attacks, which can expose more friendships that users are not willing to share. Compared with independent attackers, collusion attackers share query results by cooperating with each other. In this article, we propose a resistance strategy against collusion attacks to protect the friendship privacy. The proposed trust metric is based on users’ behaviors and is combined with Shamir’s secret sharing system, which can transform friendships into secrets. Through secret distribution and reconfiguration, only the participants who meet the query requirements can successfully reconstruct the secret, while the participants who do not meet the query conditions cannot successfully obtain the secret fragments even if they obtain the secret fragments. Experiments are conducted to verify the effectiveness of the proposed strategy and proved that this strategy can greatly limit the number of malicious attackers, greatly reduce the probability of successful collusion attacks, and reduce the number of victims.

QSMVM: QoS-Aware and Social-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETS

A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Real-time multimedia services, such as video-streaming over MANETs, offers very promising applications, e.g., two members of a group of tourists who want to share a video transmitted through the MANET they form, a video-streaming service deployed over a MANET where users watch a film, among other examples. On the other hand, social web technologies, where people actively interact online with others through social networks, are leading to a socialization of networks. Information of interaction among users is being used to provide socially-enhanced software. To achieve this, we need to know the strength of the relationship between a given user and each user they interact with. This strength of the relationship can be measured through a concept called tie strength (TS), first introduced by Mark Granovetter in 1973. In this article, we modify our previous proposal named multipath multimedia dynamic source routing (MMDSR) protocol to include a social metric TS in the decisions taken by the forwarding algorithm. We find a trade-off between the quality of service (QoS) and the trust level between users who form the forwarding path in the MANET. Our goal is to increase the trust metric while the QoS is not affected significantly.

Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities

With tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power a variety of applications. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed in order to improve the trustworthiness of a deep neural network. We further extend the concept of trust densities with the notion of conditional trust densities.

Construction of Internet of things trusted group based on multidimensional attribute trust model

Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

An Energy Efficient Trust Aware Opportunistic Routing Protocol for Wireless Sensor Network

As the wireless sensor networks (WSN) are gaining popularity the need of reliable delivery of data packets becomes more important. The reliable delivery is only possible when the routing protocols are efficient and secure. Because of lack of resources it is not possible to use existing cryptosystems to provide security in WSN. But, trust aware routing can provide the security with lesser resources, which become popular in last three to four years. In this paper, a new energy efficient and trust aware reliable opportunistic routing (TAEROR) protocol is proposed. The protocol consists of a trust metric and also a relay selection algorithm. The trust aware metric detects the malicious nodes on the basis of forwarding sincerity, energy consumption and acknowledgement sincerity. Relay selection algorithms avoid these malicious nodes to get selected in the routing process. The protocol is simulated and compared to existing trust aware routing protocols. Proposed protocol TEAROR presents better results than the other compared protocols.

Multi-dimensional trust aware routing for clustered IOT framework

With the increased interest in the utilization of smart applications, the IoT has gained a lot of popularity in the real world scenario. Due to the integration of different heterogeneous devices in a single network, various types of security issues will arise in the IoT. To ensure more security in IoT, this paper proposes a new trust aware routing framework based on the inherent communication and data properties of devices connected in the network. A new trust metric is derived in this paper by combining two different trusts based on the communication between nodes and the information passing through them. Further to achieve a prolonged network lifetime with optimal resource conservation, a clustering based communication strategy is accomplished. Extensive simulations are carried out by varying the malicious nature of network and the performance is measured through the metrics, packet loss ratio, packet delivery ratio, delay, network lifetime and average energy consumption.