Random projection to preserve patient privacy

With the availability of accessible and widely used cloud services, it is natural that large components of healthcare systems migrate to them; for example, patient databases can be stored and processed in the cloud. Such cloud services provide enhanced flexibility and additional gains, such as availability, ease of data share, and so on. This trend poses serious threats regarding the privacy of the patients and the trust that an individual must put into the healthcare system itself. Thus, there is a strong need of privacy preservation, achieved through a variety of different approaches. In this paper, we study the application of a random projection-based approach to patient data as a means to achieve two goals: (1) provably mask the identity of users under some adversarial-attack settings, (2) preserve enough information to allow for aggregate data analysis and application of machine-learning techniques. As far as we know, such approaches have not been applied and tested on medical data. We analyze the tradeoff between the loss of accuracy on the outcome of machine-learning algorithms and the resilience against an adversary. We show that random projections proved to be strong against known input/output attacks while offering high quality data, as long as the projected space is smaller than the original space, and as long as the amount of leaked data available to the adversary is limited

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

This is the author accepted manuscript. The final version is available from the Royal Society via the DOI in this record.Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli--sodium chloride (NaCl), sulfuric acid (H₂SO₄) and ozone (O₃). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in future.The work reported in this paper was supported by project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

Forward and Inverse Modelling Approaches for Prediction of Light Stimulus from Electrophysiological Response in Plants

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the input light stimulus (in terms of on-off timing, duration and intensity) from the measured electrical response - leading to an inverse problem. We explored two major classes of system estimators to develop dynamical models - linear and nonlinear - and their several variants for establishing a forward and also an inverse relationship between the light stimulus and plant electrical response. The best class of models are given by the Nonlinear Hammerstein-Wiener (NLHW) estimator showing good data fitting results over other linear and nonlinear estimators in a statistical sense. Consequently, a few set of models using different functional variants of NLHW has been developed and their accuracy in detecting the on-off timing and intensity of the input light stimulus are compared for 19 independent plant datasets (including 2 additional species viz. Zamioculcas zamiifolia and Cucumis sativus) under similar experimental scenario.The work reported in this paper was supported by project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

Drift Removal in Plant Electrical Signals via IIR Filtering Using Wavelet Energy

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Plant electrical signals often contains low frequency drifts with or without the application of external stimuli. Quantification of the randomness in plant signals in a stimulus-specific way is hindered because the knowledge of vital frequency information in the actual biological response is not known yet. Here we design an optimum Infinite Impulse Response (IIR) filter which removes the low frequency drifts and preserves the frequency spectrum corresponding to the random component of the unstimulated plant signals by bringing the bias due to unknown artifacts and drifts to a minimum. We use energy criteria of wavelet packet transform (WPT) for optimization based tuning of the IIR filter parameters. Such an optimum filter enforces that the energy distribution of the pre-stimulus parts in different experiments are almost overlapped but under different stimuli the distributions of the energy get changed. The reported research may popularize plant signal processing, as a separate field, besides other conventional bioelectrical signal processing paradigms.This work was supported by EU FP7 project PLants Employed As SEnsor Devices (PLEASED), EC grant agreement number 296582

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

On Refining Design Patterns for Smart Contracts

The need for a Blockchain Oriented Software Engineering (BOSE) has been recognized in several research papers. Design Patterns are considered among the main and compelling areas to be developed in BOSE. Anyway, design patterns need to be enhanced with some additional fields to better support the specific needs of Blockchain development. In this paper, we discuss the use of Solidity design patterns applied to a water management use case and we introduce specific fields in their description, aiming at offering to Blockchain developers more support in the critical decisions to build efficient decentralized applications

On Refining Design Patterns for Smart Contracts

The need for a Blockchain Oriented Software Engineering (BOSE) has been recognized in several research papers. Design Patterns are considered among the main and compelling areas to be developed in BOSE. Anyway, design patterns need to be enhanced with some additional fields to better support the specific needs of Blockchain development. In this paper, we discuss the use of Solidity design patterns applied to a water management use case and we introduce specific fields in their description, aiming at offering to Blockchain developers more support in the critical decisions to build efficient decentralized applications

The Blockchain Quadrilemma: When Also Computational Effectiveness Matters

Ethereum's founder Buterin raised the challenge of solving the blockchain Trilemma towards a decentralized computer that could together achieve high degrees of security, scalability, and decentralization. Later on, Algorand claimed to have resolved Buterin's blockchain Trilemma and is nowadays increasingly adopted by designers of decentralized computations. Motivated by the need of selecting a blockchain to run some decentralized computations, we observe the limitations of using the Trilemma as benchmark, and we propose as alternative a Quadrilemma that takes into account also computational effectiveness, namely the capability of running non-trivial decentralized computations at affordable costs. For concreteness, motivated by the current trends of using blockchains for the management of non-fungible tokens (NFTs) related to highly desired items (i.e., NFTs for art), we consider the use case of decentralized auctions in various scenarios that mainly differ on the desired degree of confidentiality. Our contribution gives the following insights. Except very limited cases where also Bitcoin can be taken into account (i.e., when latency is not a big deal and only notarization is required), Algorand can often be the right choice as long as decentralized computations consist of basic operations only. Instead, Ethereum is advisable when more sophisticated computations are required, in particular when ad-hoc cryptographic tasks are essential, and one can afford the involved costs and latency. Focusing on those three blockchains, the state of affairs about resolving the blockchain Quadrilemma is somewhat unsatisfying. Even in natural cases where computations and storage requirements for a smart contract are low (e.g., public-key encryption), none of those decentralized computers achieves simultaneously low cost and fast transaction confirmations

Directed Diffusion Light: Low Overhead Data Dissemination in Wireless Sensor Networks

In this paper we introduce Directed Diffusion Light, a variant of the well-known protocol Directed Diffusion (DD), which results in significant savings in terms of exchanged control messages and energy consumption, and improvements in network lifetime. Directed Diffusion Light defines local rules to generate a sparse logical topology over which DD can be run. This decreases the costs associated to the required DD periodic floodings. Ns-2 based simulation results show that, when 300 sensor nodes are deployed over a squared area of side 200m Directed Diffusion Light is able to increase the network lifetime four times, to halve the average energy consumption, and to reduce the control overhead to one third the one of DD. ©2005 IEEE