Data security issues in cloud scenarios

The amount of data created, stored, and processed has enormously increased in the last years. Today, millions of devices are connected to the Internet and generate a huge amount of (personal) data that need to be stored and processed using scalable, efficient, and reliable computing infrastructures. Cloud computing technology can be used to respond to these needs. Although cloud computing brings many benefits to users and companies, security concerns about the cloud still represent the major impediment for its wide adoption. We briefly survey the main challenges related to the storage and processing of data in the cloud. In particular, we focus on the problem of protecting data in storage, supporting fine-grained access, selectively sharing data, protecting query privacy, and verifying the integrity of computations

Development of lens opacities with peculiar characteristics in patients affected by thalassemia major on chelating treatment with deferasirox (ICL670) at the Pediatric Clinic in Monza, Italy.

About the 11–14% of patients with thalassemia major (TM) treated with deferasirox (DFO) develops retinopathy and/or lens opacities with an unclear pathogenesis but with a clear age related pattern.[1][1],[2][2] Possible causes can be either iron overload itself or DFO toxicity, with variou

Autologous stem cell transplantation for aggressive non-Hodgkin's lymphomas

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Privacy in Microdata Release: Challenges, Techniques, and Approaches

Releasing and disseminating useful microdata while ensuring that no personal or sensitive information is improperly exposed is a complex problem, heavily investigated by the scientific community in the past couple of decades. Various microdata protection approaches have then been proposed, achieving different privacy requirements through appropriate protection techniques. This chapter discusses the privacy risks that can arise in microdata release and illustrates some well-known privacy-preserving techniques and approaches

Supporting Application Requirements in Cloud-based IoT Information Processing

IoT infrastructures can be seen as an interconnected network of sources of data, whose analysis and processing can be beneficial for our society. Since IoT devices are limited in storage and computation capabilities, relying on external cloud providers has recently been identified as a promising solution for storing and managing IoT data. Due to the heterogeneity of IoT data and applicative scenarios, the cloud service delivery should be driven by the requirements of the specific IoT applications. In this paper, we propose a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing. Our solution allows a subject with an authority over an IoT infrastructure to formulate conditions that the provider must satisfy in service provisioning, and computes a SLA based on these conditions while accounting for possible dependencies among them. We also illustrate a CSP-based formulation of the problem of computing a SLA, which can be solved adopting off-the-shelves CSP solvers

Supporting User Requirements and Preferences in Cloud Plan Selection

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth. An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security. While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs. In this paper, we address the problem of supporting users in cloud plan selection. We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express. The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans

A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user\u2019s desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users\u2019 desiderata, and hence to help users in the cloud service selection process

Efficacy and safety of therapies for COVID-19 in pregnancy: a systematic review and meta-analysis

Background: Clinical evidence suggests that pregnant women are more vulnerable to COVID-19, since they are at increased risk for disease progression and for obstetric complications, such as premature labor, miscarriage, preeclampsia, cesarean delivery, fetal growth restriction and perinatal death. Despite this evidence, pregnant women are often excluded from clinical trials, resulting in limited knowledge on COVID-19 management. The aim of this systematic review and meta-analysis is to provide better evidence on the efficacy and safety of available COVID-19 treatment in pregnant women. Methods: Four authors searched major electronic databases from inception until 1 st November-2022 for controlled trials/observational studies, investigating outcomes after the administration of anti-SARS-CoV-2 treatments in pregnant women affected by COVID-19. The analyses investigated the cumulative incidence of delivery and maternal outcomes in pregnant women, comparing those taking active medication vs standard care. Risk ratios (RRs) with 95% confidence intervals were calculated. Statistical significance was assessed using the random effects model and inverse-variance method. This systematic review and meta-analysis was conducted in accordance with the updated 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The protocol has been registered in Prospero (number registration: CRD42023397445). Results: From initially 937 non duplicate records, we assessed the full texts of 40 articles, finally including ten studies. In six studies, including 1627 patients, the use of casirivimab/imdevimab (CAS/IMD), remdesivir, and IFN-alpha 2b significantly decreased the need of cesarean section ((RR = 0.665; 95%CI: 0.491–0.899; p = 0.008; I 2 = 19.5%;) (Table 1, (Fig. 1). Treatments did not decrease the risk of preterm delivery, admission to neonatal ICU, or stillbirth/perinatal loss (p-values > 0.50 for all these outcomes) and did not prevent the progression of disease towards severe degrees (k = 8; 2,374 pregnant women; RR = 0.778; 95%CI: 0.550–1.099; p = 0.15; I 2 = 0%). Moreover, the use of medications during pregnancy did not modify the incidence of maternal death in two studies (Table 2). Conclusions: To our analysis, CAS/IMD, remdesivir, and IFN alpha 2b reduced the number of cesarean sections but demonstrated no effect on disease progression and other obstetric and COVID-19 related outcomes. The inability to evaluate the influence of viral load on illness development in pregnant women was attributed to lack of data. In our systematic review, no major side effects were reported. Though, it is essential for the medical community to focus more on clinical trials and less on episodic case reports and case series, with standardization of fetal and maternal outcomes

Supporting Users in Cloud Plan Selection

Cloud computing is a key technology for outsourcing data and applications to external providers. The current cloud market offers a multitude of solutions (plans) differing from one another in terms of their characteristics. In this context, the selection of the right plan for outsourcing is of paramount importance for users wishing to move their data/applications to the cloud. The scientific community has then developed different models and tools for capturing users\u2019 requirements and evaluating candidate plans to determine the extent to which each of them satisfies such requirements. In this chapter, we illustrate some of the existing solutions proposed for cloud plan selection and for supporting users in the specification of their (crisp and/or fuzzy) needs

Data protection in Cloud scenarios

We present a brief overview of the main challenges related to data protection that need to be addressed when data are stored, processed, or managed in the cloud. We also discuss emerging approaches and directions to address such challenges