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The control of exploratory and manipulative procedures in Teleoperation and Virtual Environments requires the availability of adequate advanced interfaces capable not only of recording the movements of the human hands and arms, but also of replicating sensations of contact and collisions. In this paper the problem of replicating external forces acting against the remote/virtual a r m is addressed. The design of an arm exoskeleton system developed an our laboratory is presented. The exoskeleton consists of a 7 actuated and sensorized DOF mechanical structure wrapping up completely the human arm and directly supported by the shoulders and the trunk of the human operator. Emphasis is given t o the implemented control procedures and t o the description of the transputer-based control architecture.

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Placing critical data in the hands of a cloud provider should come with the guarantee of security and availability for data at rest, in motion, and in use. Several alternatives exist for storage services, while data confidentiality solutions for the database as a service paradigm are still immature. We propose a novel architecture that integrates cloud database services with data confidentiality and the possibility of executing concurrent operations on encrypted data. This is the first solution supporting geographically distributed clients to connect directly to an encrypted cloud database, and to execute concurrent and independent operations including those modifying the database structure. The proposed architecture has the further advantage of eliminating intermediate proxies that limit the elasticity, availability, and scalability properties that are intrinsic in cloud-based solutions. The efficacy of the proposed architecture is evaluated through theoretical analyses and extensive experimental results based on a prototype implementation subject to the TPC-C standard benchmark for different numbers of clients and network latencies.

The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning methods, we identify and model the real capabilities and circumstances that are necessary for an attacker to carry out a feasible and successful adversarial attack. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.

Designing secure, scalable, and resilient IoT networks is a challenging task because of resource-constrained devices and no guarantees of reliable network connectivity. Fog computing improves the resiliency of IoT, but its security model assumes that fog nodes are fully trusted. We relax this latter constraint by proposing a solution that guarantees confidentiality of messages exchanged through semi-honest fog nodes thanks to a lightweight proxy re-encryption scheme. We demonstrate the feasibility of the solution by applying it to IoT networks of low-power devices through experiments on microcontrollers and ARM-based architectures.

Abstract. Typical Cloud database services guarantee high availability and scalability, but they rise many concerns about data confidentiality. Combining encryption with SQL operations is a promising approach although it is characterized by many open issues. Existing proposals, which are based on some trusted intermediate server, limit availability and scalability of original cloud database services. We propose an alternative architecture that avoids any intermediary component, thus achieving availability and scalability comparable to that of unencrypted cloud database services. Moreover, our proposal guarantees data consistency in scenarios in which independent clients concurrently execute SQL queries, and the structure of the database can be modified.

The diffusion of cloud database services requires a lot of efforts to improve confidentiality of data stored in external infrastructures. We propose a novel scheme that integrates data encryption with users access control mechanisms. It can be used to guarantee confidentiality of data with respect to a public cloud infrastructure, and to minimize the risks of internal data leakage even in the worst case of a legitimate user colluding with some cloud provider personnel. The correctness and feasibility of the proposal is demonstrated through formal models, while the integration in a cloud-based architecture is left to future work.