| 1. |
- Carraminana, Albert, et al.
(författare)
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Rationale and Study Design for an Individualized Perioperative Open Lung Ventilatory Strategy in Patients on One-Lung Ventilation (iPROVE-OLV)
- 2019
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Ingår i: Journal of Cardiothoracic and Vascular Anesthesia. - : W B SAUNDERS CO-ELSEVIER INC. - 1053-0770 .- 1532-8422. ; 33:9, s. 2492-2502
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The aim of this clinical trial is to examine whether it is possible to reduce postoperative complications using an individualized perioperative ventilatory strategy versus using a standard lung-protective ventilation strategy in patients scheduled for thoracic surgery requiring one-lung ventilation. Design: International, multicenter, prospective, randomized controlled clinical trial. Setting: A network of university hospitals. Participants: The study comprises 1,380 patients scheduled for thoracic surgery. Interventions: The individualized group will receive intraoperative recruitment maneuvers followed by individualized positive end-expiratory pressure (open lung approach) during the intraoperative period plus postoperative ventilatory support with high-flow nasal cannula, whereas the control group will be managed with conventional lung-protective ventilation. Measurements and Main Results: Individual and total number of postoperative complications, including atelectasis, pneumothorax, pleural effusion, pneumonia, acute lung injury; unplanned readmission and reintubation; length of stay and death in the critical care unit and in the hospital will be analyzed for both groups. The authors hypothesize that the intraoperative application of an open lung approach followed by an individual indication of high-flow nasal cannula in the postoperative period will reduce pulmonary complications and length of hospital stay in high-risk surgical patients. (C) 2019 Published by Elsevier Inc.
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| 2. |
- Abril, Daniel, et al.
(författare)
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Spherical Microaggregation : Anonymizing Sparse Vector Spaces
- 2015
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Ingår i: Computers & security (Print). - : Elsevier. - 0167-4048 .- 1872-6208. ; 49, s. 28-44
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Tidskriftsartikel (refereegranskat)abstract
- Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets.
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| 3. |
- Abril, Daniel, et al.
(författare)
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Supervised Learning Using a Symmetric Bilinear Form for Record Linkage
- 2015
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Ingår i: Information Fusion. - : Elsevier. - 1566-2535 .- 1872-6305. ; 26, s. 144-153
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Tidskriftsartikel (refereegranskat)abstract
- Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk.In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions.
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| 4. |
- Cucurull, Jordi, et al.
(författare)
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An efficient and secure agent code distribution service
- 2010
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Ingår i: Software: Practice and Experience. - : John Wiley & Sons. - 0038-0644 .- 1097-024X. ; 40:4, s. 363-386
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Tidskriftsartikel (refereegranskat)abstract
- Mobile agents (MAs) are autonomous computing entities that dwell in agent platforms and have the ability to move to different locations as needed. They are typically composed of code, data, and a state. The performance of MA migrations is always penalized by the need of carrying these components to each visited location. In contrast to the agent data and state, the agent code is static during the whole agent life. Therefore, optimizing the agent code management, the agent migration performance can be improved. The main contribution of this article is the definition of a global cache service to efficiently and securely deal with the distribution of agent code. An implementation of the service has been developed, and its benefits have been extensively demonstrated by a set of performance tests carried out in different scenarios.
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| 5. |
- Galván, Edgar, et al.
(författare)
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Agents in a privacy-preserving world
- 2021
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Ingår i: Transactions on Data Privacy. - : University of Skövde. - 1888-5063 .- 2013-1631. ; 14:1, s. 53-63
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Tidskriftsartikel (refereegranskat)abstract
- Privacy is a fluid concept. It is both difficult to define and difficult to achieve. The large amounts of data currently available at hands of companies and administrations increase individual concerns on what is yet to be known about us. For the sake of penalisation and customisation, we often need to give up and supply information that we consider sensitive and private. Other sensitive information is inferred from information that seems harmless. Even when we explicitly require privacy and anonymity, profiling and device fingerprinting may disclose information about us leading to reidentification. Mobile devices and the internet of things make keeping our live private still more difficult. Agent technologies can play a fundamental role to provide privacy-aware solutions. Agents are inherently suitable in the heterogeneous environment in which our devices work, and we can delegate to them the task of protecting our privacy. Agents should be able to reason about our privacy requirements, and may collaborate (or not) with other agents to help us to achieve our privacy goals. We are presented in the connected world with multiple interests, profiles, and also through multiple agentified devices. We envision our agentified devices to collaborate among themselves and with other devices so that our privacy preferences are satisfied. We believe that this is an overlooked field. Our work intends to start shedding some light on the topic by outlining the requirements and challenges where agent technologies can provide a decisive role.
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| 6. |
- Kaya, Sema Kayapinar, et al.
(författare)
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Dynamic Features Spaces and Machine Learning: Open Problems and Synthetic Data Sets
- 2020
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Ingår i: Integrated Uncertainty in Knowledge Modelling and Decision Making. - Cham : Springer Nature. - 9783030625085 - 9783030625092 ; , s. 125-136
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Konferensbidrag (refereegranskat)abstract
- Dynamic feature spaces appear when different records or instances in databases are defined in terms of different features. This is in contrast with usual (static) feature spaces in standard databases, where the schema of the database is known and fixed. Then, all records in the database have the same set of variables, attributes or features. Dynamic feature mining algorithms are to extract knowledge from data on dynamic feature spaces. As an example, spam detection methods have been developed from a dynamic feature space perspective. Words are taken as features and new words appearing in new emails are, therefore, considered new features. In this case, the problem of spam detection is represented as a classification problem (a supervised machine learning problem).The relevance of dynamic feature spaces is increasing. The large amounts of data currently available or received by systems are not necessarily described using the same feature spaces. This is the case of distributed databases with data about customers, providers, etc. Industry 4.0, Internet of Things, and RFIDs are and will be a source of data in dynamic feature spaces. New sensors added in an industrial environment, new devices connected into a smart home, new types of analysis and new types of sensors in healthcare, all are examples of dynamic feature spaces. Machine learning algorithms are needed to deal with these type of scenarios.In this paper we motivate the interest for dynamic feature mining, we give some examples of scenarios where these techniques are needed, we review some of the existing solutions and its relationship with other areas of machine learning and data mining (e.g., incremental learning, concept drift, topic modeling), we discuss some open problems, and we discuss synthetic data generation for this type of problem.
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| 7. |
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| 8. |
- Torra, Vicenç, et al.
(författare)
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An Overview of the Use of Clustering for Data Privacy
- 2016
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Ingår i: Unsupervised Learning Algorithms. - Cham : Springer. - 9783319242095 - 9783319242118 ; , s. 237-251
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Bokkapitel (refereegranskat)abstract
- In this chapter we review some of our results related to the use of clustering in the area of data privacy. The paper gives a brief overview of data privacy and, more specifically, on data driven methods for data privacy and discusses where clustering can be applied in this setting. We discuss the role of clustering in the definition of masking methods, and on the calculation of information loss and data utility.
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| 9. |
- Torra, Vicenç, et al.
(författare)
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Attribute disclosure risk for k-anonymity : the case of numerical data
- 2023
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Ingår i: International Journal of Information Security. - : Springer Nature. - 1615-5262 .- 1615-5270. ; 22:6, s. 2015-2024
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Tidskriftsartikel (refereegranskat)abstract
- k-Anonymity is one of the most well-known privacy models. Internal and external attacks were discussed for this privacy model, both focusing on categorical data. These attacks can be seen as attribute disclosure for a particular attribute. Then, p-sensitivity and p-diversity were proposed as solutions for these privacy models. That is, as a way to avoid attribute disclosure for this very attribute. In this paper we discuss the case of numerical data, and we show that attribute disclosure can also take place. For this, we use well-known rules to detect sensitive cells in tabular data protection. Our experiments show that k-anonymity is not immune to attribute disclosure in this sense. We have analyzed the results of two different algorithms for achieving k-anonymity. First, MDAV as a way to provide microaggregation and k-anonymity. Second, Mondrian. In fact, to our surprise, the number of cells detected as sensitive is quite significant, and there are no fundamental differences between Mondrian and MDAV. We describe the experiments considered, and the results obtained. We define dominance rule compliant and p%-rule compliant k-anonymity for k-anonymity taking into account attribute disclosure. We conclude with an analysis and directions for future research.
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| 10. |
- Torra, Vicenç, et al.
(författare)
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Big Data Privacy and Anonymization
- 2016
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Ingår i: Privacy and Identity Management. Facing up to Next Steps. - Cham : Springer. - 9783319557823 - 9783319557830 ; , s. 15-26
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Bokkapitel (refereegranskat)abstract
- Data privacy has been studied in the area of statistics (statistical disclosure control) and computer science (privacy preserving data mining and privacy enhancing technologies) for at least 40 years. In this period models, measures, methods, and technologies have been developed to effectively protect the disclosure of sensitive information.The coming of big data, with large volumes of data, dynamic and streaming data, poses new challenges to the field. In this paper we will review some of these challenges and propose some lines of research in the field.
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