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Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node’s infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners—This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles’ security and privacy. Moreover, vehicles’ information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles’ data transmission during the forensic service. To this end, this paper proposes a lightweight conditional anonymous authentication scheme for forensic services in IoV, which is developed based on the pairing-free technique to achieve secure anonymous authentication with high efficiency. This paper also designs a user tracing mechanism, incentive mechanism, and pseudonym update mechanism to realize safe and effective forensic service in IoV.

Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates $(t,n)$(t,n) threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our $(t,n)$(t,n) threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.

Trojan attacks on deep neural networks (DNNs) exploit a backdoor embedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploiting STRong Intentional Perturbation of inputs that is a multi-domain input-agnostic Trojan detection defence across Vision, Text and Audio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.

By advances in cloud computing, users are allowed to remotely store their data in the cloud, manage the stored data without limitation of time and place, and give rights to visitors that want to access to their data. As may no longer possess data physically, the data owner has to ensure the integrity of the data with the public key given by Public key infrastructure (PKI). However, there are many security risks of the traditional PKI and the certificate management is complex. We utilize elliptic curve group to propose a certificateless signature to solve the above problem. To check the integrity of files stored in the cloud, we design a certificateless public verification mechanism based on the signature and further extend it to support batch auditing tasks. Meanwhile, an efficient key updating method is proposed to provide visitors a friendly data success environment. The security analysis proves that the proposed scheme is secure under the discrete logarithm assumption. Extensive theoretical analyses and experimental results show the effectiveness of the proposed scheme.

In cloud storage systems, data security management is becoming a serious matter. Big data and accessibility power is increasingly high, though the benefits are clear, such a service is also relinquishing users' physical possession of their outsourced data, which inevitably poses new security risks toward the correctness of the data in cloud. As a result, cloud storage security has become one of the driving components in Cloud Computing regarding to data manipulation trust on both hosting center and on-transit. This paper proposes a TPM-Based Security over Multi-Cloud Storage Architecture (MCSA) grounded on Erasure Codes to apply root of trust based on hardware authenticity. An erasure codes such as Reed-Solomon, is capable of assuring stability in storage costs with best practice to guarantee data accessibility failure recovery. A Multi-Cloud Control Node manages other Control Nodes evolved in the cloud; this work introduces TPM-Based Security functions per Control node in the architecture. This concept will resolve a number of storage security issues, hence Cloud Computing adoption.

ABSTRACTCompressed sensing (CS) is a new signal acquisition method that can do sampling and compression of signals simultaneously. In order to reduce the signal reconstruction time of CS algorithms and lower the growth rate of the reconstruction time when increasing the size of signals, this paper proposes the algorithm of block whole orthogonal matching pursuit (BWOMP), which is a fast CS algorithm based on the method of orthogonal matching pursuit (OMP) for two-dimension (2D) signals. BWOMP defines a measurement parameter named whole-correlation. At each iteration, instead of computing the correlation between each atom and 1D residuals, the whole-correlation is computed as the correlation between the atom and the 2D residuals. After that, an approximation of the 2D signal is generated directly by BWOMP. By reducing the number of the iterations, this method can significantly lower the computational complexity. On the other hand, BWOMP introduces the concept of block compressed sensing (BCS), and redesigns the block size and the observation matrix. BCS reduces the consumption of computational resources (i.e. memory and CPU cycles) by reducing the size of variables (especially the matrixes). The experimental comparisons show that, in comparison with OMP, BWOMP can save at least 80% reconstruction time, which makes the increasing rate of reconstruction time linear. The results indicate that the proposed algorithm may have great performance advantage for complex cases.

• This paper propose a PRE based trusted authorization scheme for nodes on CloudIoT. • It can update the authorization assuredly when some nodes are hijacked. • In this scheme, cloud servers are fully used than the other work. • The analysis shows it has better efficiency than other works with the same properties. In CloudIoT platform, the data is collected and shared by different nodes of Internet of Things (IoT), and data is processed and stored based on cloud servers. It has increased the abilities of IoT on information computation. Meanwhile, it also has enriched the resource in cloud and improved integration of the Internet and human world. All of this offer advantages as well as the new challenges of information security and privacy protection. As the energy limitation of the nodes in IoT, they are particularly vulnerable. It is much easier to hijack the nodes than to attack the data center for hackers. Thus, it is a crucial and urgent issue to realize the trusted update of authorization of nodes. When some nodes are hijacked, both of the behaviors to upload data to servers and to download information from servers should be forbidden. Otherwise, it might cause the serious damage to the sensitive data and privacy of servers. In order to solve this problem, we proposed a Proxy Re-encryption based Trusted Authorization scheme for nodes on CloudIoT (PRTA). PRTA is based on the proxy re-encryption (PRE), and the cloud server will play the roles of data storing and re-encrypting, which would reach the full potential of cloud computing and reduce the cost of nodes. The node's status is taken as one of the parameters for data re-encryption and it is under the authorization servers' control, which could ensure the security and reliability of the data and be beneficial for the privacy protection in CloudIoT. Also, the authorization servers are divided into the downloading and uploading kinds, which will make the application range much wider. [ABSTRACT FROM AUTHOR]