Biblio

The Internet of Things is progressively turning into a pervasive computing service, needing enormous volumes of data storage and processing. However, due to the distinctive properties of resource constraints, self-organization, and short-range communication in Internet of Things (IoT), it always adopts to cloud for outsourced storage and computation. This integration of IoT with cloud has a row of unfamiliar security challenges for the data at rest. Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud computing services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). Time varying workload and data intensive IoT applications are vulnerable to encounter challenges while using cloud computing services. Additionally, the encryption techniques and third-party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for IoT applications. In this paper, we perform an analysis study to investigate the challenges and strategies adapted by Cloud Computing to facilitate a safe transition of IoT applications to the Cloud.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

In the privacy protection method based on user collaboration, all participants and collaborators must share the maximum anonymity value set in the anonymous group. No user can get better quality of service by reducing the anonymity requirement. In this paper, a privacy protection algorithm random-QBE, which divides query information into blocks and exchanges randomly, is proposed. Through this method, personalized anonymity, query diversity and location anonymity in user cooperative privacy protection can be realized. And through multi-hop communication between collaborative users, this method can also satisfy the randomness of anonymous location, so that the location of the applicant is no longer located in the center of the anonymous group, which further increases the ability of privacy protection. Experiments show that the algorithm can complete the processing in a relatively short time and is suitable for deployment in real environment to protect user's location privacy.

The Internet of Things (IoT) promises to tackle a range of environmental challenges and deliver large efficiency gains in industry by embedding computational intelligence, sensing and control in our physical environment. Multiple independent parties are increasingly seeking to leverage shared IoT infrastructure, using a similar model to the cloud, and thus require constrained IoT devices to become microservice-hosting platforms that can securely and concurrently execute their code and interoperate. This vision demands that heterogeneous services, peripherals and platforms are provided with an expanded set of security guarantees to prevent third-party services from hijacking the platform, resource-level access control and accounting, and strong isolation between running processes to prevent unauthorized access to third-party services and data. This paper introduces Polyglot CerberOS, a resource-secure operating system for multi-tenant IoT devices that is realised through a reconfigurable virtual machine which can simultaneously execute interoperable services, written in different languages. We evaluate Polyglot CerberOS on IETF Class-1 devices running both Java and C services. The results show that interoperability and strong security guarantees for multilingual services on multi-tenant commodity IoT devices are feasible, in terms of performance and memory overhead, and transparent for developers.