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Regulations set out strict restrictions on processing personal data. ML models must also adhere to these restrictions, as it may be possible to infer personal information from trained models. In this paper, we demonstrate the use of two novel AI Privacy tools in a real-world healthcare application.

Since the emergence of GDPR, several industries and sectors are setting informatics solutions for fulfilling these rules. The Health sector is considered a critical sector within the Industry 4.0 because it manages sensitive data, and National Health Services are responsible for managing patients’ data. European NHS are converging to a connected system allowing the exchange of sensitive information cross different countries. This paper defines and implements a set of tools for extending the reference architectural model industry 4.0 for the healthcare sector, which are used for enhancing GDPR compliance. These tools are dealing with data sensitivity and data hiding tools A case study illustrates the use of these tools and how they are integrated with the reference architectural model.

The EU General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) mandate the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as deep neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features of the runtime data. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy, based on knowledge distillation approaches. We show that, in some cases, less data may be collected while preserving the exact same level of model accuracy as before, and if a small deviation in accuracy is allowed, even more generalizations of the input features may be performed. We also demonstrate that when collecting the features dynamically, the generalizations may be even further improved. This method enables organizations to truly minimize the amount of data collected, thus fulfilling the data minimization requirement set out in the regulations.

Cloud- and service-oriented computing paradigms are intrinopaque to their users, as they cannot inspect providers’ implementations, and important concerns about aspects like security, compliance, dependability can arise. Therefore, users have to make trust decisions with respect to software providers, with the hope that there will not be any detrimental consequences. To contrast this situation, the paper proposes a framework to define, assess, monitor and make explicit the elements of a service that render it trustworthy. This paper relies on a number of recent scientific contributions, and aims at supporting informed decisions on obscure service implementations by machine-understandable statements about their objective (trustworthiness) characteristics. Such statements would innovate upon many aspects of service operations, from discovery to composition, deployment and monitoring. To demonstrate this, the paper presents a concept for a Trustworthy Service Marketplace.

Virtualization technology has many attractive qualities including improved security, reliability, scalability, and resource sharing/management. As a result, virtualization has been deployed on an array of platforms, from mobile devices to high end enterprise servers. In this paper, we present a novel approach to working at a virtualization interface, performing workload characterization equipped with the information available at the virtual machine monitor (VMM) interface. Due to the semantic gap between the raw VMM-level data available and the true application behavior, we employ the power of regression techniques to extract meaningful information about a workload's behavior. We also demonstrate that the information available at the VMM level still retains rich workload characteristics that can be used to identify application behavior. We show that we are able to capture enough information about a workload to characterize and decompose it into a combination of CPU, memory, disk I/O, and network I/O-intensive components. Dissecting the behavior of a workload in terms of these components, we can develop significant insight into the behavior of any application. Workload characterization can be used for online performance monitoring, workload scheduling, workload trending, virtual machine (VM)health monitoring, and security analysis. We can also consider how VMM-based workload profiles can be used to detect anomalous behavior in virtualized environments by comparing a model of potentially malicious execution to that of normal execution.

Virtualization is becoming an increasingly popular service hosting platform. Recently, intrusion detection systems (IDSs) which utilize virtualization have been introduced. One particular challenge present in current virtualization-based IDS systems is considered in this paper. IDS systems are commonly faced with high-dimensionality imbalanced data. Improved feature selection methods are needed to achieve more accurate detection when presented with imbalanced data. These methods must select the right set of features which will lead to a lower number of false alarms and higher correct detection rates. In this paper we propose a new Boosting-based feature selection that evaluates the relative importance of individual features using the fractional absolute confidence that Boosting produces. Our approach accounts for the sample distributions by optimizing for the area under the Receive Operating Characteristic (ROC) curve (i.e., Area Under the Curve(AUC)). Empirical results on different commercial virtual appliances and malwares indicate that proper input feature selection is key if we want an effective virtualization-based IDS that is lightweight, efficient and effective.

Multi-dimensional imaging techniques involve the processing of high resolution images commonly used in medical, civil and remote-sensing applications. A barrier commonly encountered in this class of applications is the time required to carry out repetitive operations on large matrices. Partitioning these large datasets can help improve performance, and lends the data to more efficient parallel execution. In this paper we describe our experience exploring two novel parallelization technologies: 1) a graphical processor unit (GPU)-based approach which utilizes 128 cores on a single GPU accelerator card, and 2) a middleware approach for semi-automatic parallelization on a cluster of multiple multi-core processors. We investigate these two platforms and describe their strengths and limitations. In addition, we provide some guidance to the programmer on which platform to use when porting multi-dimensional imaging applications. Using a 3-D application taken from a clinical image reconstruction algorithm, we demonstrate the degree of speedup we can obtain from these two approaches.

We improve the lower bounds on Klarner's constant, which describes the exponential growth rate of the number of polyominoes (connected subsets of grid squares) with a given number of squares. We achieve this by analyzing polyominoes on a different surface, a so-called $\textit{twisted cylinder}$ by the transfer matrix method. A bijective representation of the "states'' of partial solutions is crucial for allowing a compact representation of the successive iteration vectors for the transfer matrix method.

We present the PARROT concept that seeks to achievehigher performance with reduced energy consumptionthrough gradual optimization of frequently executed codetraces. The PARROT microarchitectural framework integratestrace caching, dynamic optimizations and pipelinedecoupling. We employ a selective approach for applyingcomplex mechanisms only upon the most frequently usedtraces to maximize the performance gain at any givenpower constraint, thus attaining finer control of tradeoffsbetween performance and power awareness.We show that the PARROT based microarchitecture canimprove the performance of aggressively designed processorsby providing the means to improve the utilizationof their more elaborate resources. At the same time, rigorousselection of traces prior to storage and optimizationprovides the key to attenuating increases in thepower budget.For resource-constrained designs, PARROT based architecturesdeliver better performance (up to an average16% increase in IPC) at a comparable energy level,whereas the conventional path to a similar performanceimprovement consumes an average 70% more energy.Meanwhile, for those designs which can tolerate a higherpower budget, PARROT gracefully scales up to use additionalexecution resources in a uniformly efficient manner.In particular, a PARROT-style doubly-wide machinedelivers an average 45% IPC improvement while actuallyimproving the cubic-MIPS-per-WATT power awarenessmetric by over 50%.