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• Smart personal devices are assuming a fundamental role in ubiquitous communication. • Such devices provide sophisticated cameras and new visual search interfaces facilities. • Ever-increasing need for secure and resilient visual query mechanisms for these devices. • We propose an innovative secure visual search system for ubiquitous computing scenarios. • Such system is suitable for all applications requiring secure visual queries. Smart personal devices are assuming a fundamental role in the ubiquitous communication and computing arena. They provide new sophisticated cameras and new visual search interfaces and facilities that can drastically improve their presence and role in complex IoT-based critical infrastructures, such as healthcare monitoring and emergency systems, or remote access control facilities and smart authentication services. This new scenario calls for strong secure and resilient visual query mechanisms for these devices. In this work we propose an innovative secure visual search system, which is well-suited for ubiquitous computing scenarios empowered by modern smart personal devices. More precisely, we show how to insert, at the visual data acquisition time, a watermark inside the already compressed descriptor characterizing an MPEG-CDVS data stream used in visual queries, to make it possible to decode the watermark on the server side in order to improve the robustness against image-based identity spoofing. Such a security enforcement solution may be practical in several real-life applications involving visual queries performed from personal trusted devices, and it is particularly suitable in all those application domains that require performing visual queries with a high degree of security. It has been extensively tested and achieved satisfactory results: the presence of such a watermark does not affect the image matching performance and functionality. [ABSTRACT FROM AUTHOR]

Abstract Due to their decoupling and scalability properties, multicast technologies are widely adopted in all the software projects aiming at integrating off-the-shelf systems, in order to compose large-scale complex infrastructures. This also holds within the context of mission critical systems, where strong requirements for reliable and timely data sharing are imposed. In the current literature, reliable multicast is always achieved at the expenses of violations of the temporal constraints, since retransmissions are used to recover lost messages. When timeliness requirements assume the same importance as the reliability ones, the techniques based on spatial redundancy, such as forward error correction, are preferable. However, such coding techniques are scarcely adopted within multicast services due to the difficulties related to the optimal tuning of the introduced redundancy. In this paper, we present a solution applying a proper coding scheme that jointly achieves reliability and timeliness in all those scenarios involving tree-based multicasting over the Internet. Such a solution employs game theory in order to select the best locations within the multicast tree where coding operations have to be performed. We prove the quality of this solution by using a series of simulations running on OMNET++, that compare the achievable quality with respect to the optimal solution of the underlying optimization problem. [ABSTRACT FROM AUTHOR]

With the wide diffusion of cloud technologies, an ever increasing amount of sensitive data is moved on centralized network-based repository services, providing elastic outsourced storage capacity, available through remote access. This introduces new challenges associated to the security and privacy of outsourced data that has to be dynamically created, shared, updated and removed by a large number of users, characterized by different access rights and views structured according to hierarchical roles. To address such challenges, and implement secure access control policies in those application domains, several cryptographic solutions have been proposed. In particular, hierarchical key assignment schemes represent an effective solution to deal with cryptographic access control. Starting from the first proposal due to Akl and Taylor in 1983, many hierarchical key assignment schemes have been proposed. However, the highly dynamic nature of cloud-based storage solutions may significantly stress the applicability of such schemes on a wide scale. In order to overcome such limitations, in this work we provide new results on the Akl–Taylor scheme, by carefully analyzing the problem of supporting dynamic updates, as well as key replacement operations. In doing this, we also perform a rigorous analysis of the Akl–Taylor scheme in the dynamic setting characterizing storage clouds, by considering different key assignment strategies and proving that the corresponding schemes are secure with respect to the notion of key recovery. [ABSTRACT FROM AUTHOR]

Abstract The weighted possibilistic c-means algorithm is an important soft clustering technique for big data analytics with cloud computing. However, the private data will be disclosed when the raw data is directly uploaded to cloud for efficient clustering. In this paper, a secure weighted possibilistic c-means algorithm based on the BGV encryption scheme is proposed for big data clustering on cloud. Specially, BGV is used to encrypt the raw data for the privacy preservation on cloud. Furthermore, the Taylor theorem is used to approximate the functions for calculating the weight value of each object and updating the membership matrix and the cluster centers as the polynomial functions which only include addition and multiplication operations such that the weighed possibilistic c-means algorithm can be securely and correctly performed on the encrypted data in cloud. Finally, the presented scheme is estimated on two big datasets, i.e., eGSAD and sWSN, by comparing with the traditional weighted possibilistic c-means method in terms of effectiveness, efficiency and scalability. The results show that the presented scheme performs more efficiently than the traditional weighted possiblistic c-means algorithm and it achieves a good scalability on cloud for big data clustering. [ABSTRACT FROM AUTHOR]