El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The micro financial sector is a strategic element in the economy of developing countries since it facilitates the integration and development of all social classes and let the economic growth. In this point is the growth of data is high every day in sector like the micro financial, resulting from transactions and operations carried out with these companies on a daily basis. Appropriate management of the personal data privacy policies is therefore necessary because, otherwise, it will comply with personal data protection laws and regulations and let take quality information for decision-making and process improvement. The present study proposes a personal data protection maturity model based on international standards of privacy and information security, which also reveals personal data protection capabilities in organizations. Finally, the study proposes a diagnostic and tracing assessment tool that was carried out for five companies in the micro financial sector and the obtained results were analyzed to validate the model and to help in success of data protection initiatives. / Revisión por pares

Data represent a key resource for all organizations we may think of. Thus, it is not surprising that data are the main target of a large variety of attacks. Security vulnerabilities and phishing attacks make it possible for malicious software to steal business or privacy sensitive data and to undermine data availability such as in recent ransomware attacks.Apart from external malicious parties, insider attacks also pose serious threats to organizations with sensitive information, e.g., hospitals with patients’ sensitive information. Access control mechanisms are not always able to prevent insiders from misusing or stealing data as they often have data access permissions. Therefore, comprehensive solutions for data protection require combining access control mechanisms and other security techniques,such as encryption, with techniques for detecting anomalies in data accesses. In this the-sis, we develop fine-grained anomaly detection techniques for ensuring in depth protection of data from malicious software, specifically, ransomware, and from malicious insiders.While anomaly detection techniques are very useful, in many cases the data that is used for anomaly detection are very sensitive, e.g., health data being shared with untrusted service providers for anomaly detection. The owners of such data would not share their sensitive data in plain text with an untrusted service provider and this predicament undoubtedly hinders the desire of these individuals/organizations to become more data-driven. In this thesis, we have also built a privacy-preserving framework for real-time anomaly detection.

Enterprises acquire large amounts of data from a variety of sources with the goal of extracting valuable insights and enabling informed analysis. Unfortunately, organizations continue to be hindered by poor data quality as they wrangle with their data to extract value since most real datasets are rarely error-free. Poor data quality is a pervasive problem that spans across all industries causing unreliable data analysis, and costing billions of dollars. The large body of datasets, the pace of data acquisition, and the heterogeneity of data sources pose challenges towards achieving high-quality data. These challenges are further exacerbated with data privacy and data diversity requirements. In this thesis, we study and propose solutions to address data duplication, managing the trade-off between data cleaning and data privacy, and computing diverse data instances. In the first part of this thesis, we address the data duplication problem. We propose a duplication detection framework, which combines word-embeddings with constraints among attributes to improve the accuracy of deduplication. We propose a set of constraint-based statistical features to capture the semantic relationship among attributes. We showed that our techniques achieve comparative accuracy on real datasets. In the second part of this thesis, we study the problem of data privacy and data cleaning, and we present a Privacy-Aware data Cleaning-As-a-Service (PACAS) framework to protect privacy during the cleaning process. Our evaluation shows that PACAS safeguards semantically related sensitive values, and provides lower repair errors compared to existing privacy-aware cleaning techniques. In the third part of this thesis, we study the problem of finding a diverse anonymized data instance where diversity is measured via a set of diversity constraints, and propose an algorithm to seek a k-anonymous relation with value suppression as well as satisfying given diversity constraints. We conduct extensive experiments using real and synthetic data showing the effectiveness of our techniques, and improvement over existing baselines. / Thesis / Doctor of Philosophy (PhD)

Wireless sensor networks used to have a main role as a monitoring tool for environmental purposes and animal tracking. This spectrum of applications, however, has dramatically grown in the past few years. Such evolution means that what used to be application-specific networks are now multi application environments, often with federation capabilities. This shift results in a challenging environment for data privacy, mainly caused by the broadening of the spectrum of data access points and involved entities. This thesis first evaluates existing privacy preserving data aggregation techniques to determine how suitable they are for providing data privacy in this more elaborate environment. Such evaluation led to the design of the set difference attack, which explores the fact that they all rely purely on data aggregation to achieve privacy, which is shown through simulation not to be suitable to the task. It also indicates that some form of uncertainty is required in order to mitigate the attack. Another relevant finding is that the attack can also be effective against standalone networks, by exploring the node availability factor. Uncertainty is achieved via the use of differential privacy, which offers a strong and formal privacy guarantee through data perturbation. In order to make it suitable to work in a wireless sensor network environment, which mainly deals with time-series data, two new approaches to address it have been proposed. These have a contrasting effect when it comes to utility and privacy levels, offering a flexible balance between privacy and data utility for sensed entities and data analysts/consumers. Lastly, this thesis proposes a framework to assist in the design of privacy preserving data aggregation protocols to suit application needs while at the same time complying with desired privacy requirements. The framework's evaluation compares and contrasts several scenarios to demonstrate the level of flexibility and effectiveness that the designed protocols can provide. Overall, this thesis demonstrates that data perturbation can be made significantly practical through the proposed framework. Although some problems remain, with further improvements to data correlation methods and better use of some intrinsic characteristics of such networks, the use of data perturbation may become a practical and efficient privacy preserving mechanism for wireless sensor networks.

No description available.

abstract: Data privacy is emerging as one of the most serious concerns of big data analytics, particularly with the growing use of personal data and the ever-improving capability of data analysis. This dissertation first investigates the relation between different privacy notions, and then puts the main focus on developing economic foundations for a market model of trading private data. The first part characterizes differential privacy, identifiability and mutual-information privacy by their privacy--distortion functions, which is the optimal achievable privacy level as a function of the maximum allowable distortion. The results show that these notions are fundamentally related and exhibit certain consistency: (1) The gap between the privacy--distortion functions of identifiability and differential privacy is upper bounded by a constant determined by the prior. (2) Identifiability and mutual-information privacy share the same optimal mechanism. (3) The mutual-information optimal mechanism satisfies differential privacy with a level at most a constant away from the optimal level. The second part studies a market model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The value of epsilon units of privacy is measured by the minimum payment such that an individual's equilibrium strategy is to report data in an epsilon-differentially private manner. For the setting with binary private data that represents individuals' knowledge about a common underlying state, asymptotically tight lower and upper bounds on the value of privacy are established as the number of individuals becomes large, and the payment--accuracy tradeoff for learning the state is obtained. The lower bound assures the impossibility of using lower payment to buy epsilon units of privacy, and the upper bound is given by a designed reward mechanism. When the individuals' valuations of privacy are unknown to the data collector, mechanisms with possible negative payments (aiming to penalize individuals with "unacceptably" high privacy valuations) are designed to fulfill the accuracy goal and drive the total payment to zero. For the setting with binary private data following a general joint probability distribution with some symmetry, asymptotically optimal mechanisms are designed in the high data quality regime. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

Recent research shows that machine learning algorithms are highly susceptible to attacks trying to extract sensitive information about the data used in model training. These attacks called privacy attacks, exploit the model training process. Contemporary defense techniques make alterations to the training algorithm. Such defenses are computationally expensive, cause a noticeable privacy-utility tradeoff, and require control over the training process. This thesis presents a data-centric approach using data augmentations to mitigate privacy attacks. We present privacy-focused data augmentations to change the sensitive data submitted to the model trainer. Compared to traditional defenses, our method provides more control to the individual data owner to protect one's private data. The defense is model-agnostic and does not require the data owner to have any sort of control over the model training. Privacypreserving augmentations are implemented for two attacks namely membership inference and model inversion using two distinct techniques. While the proposed augmentations offer a better privacy-utility tradeoff on CIFAR-10 for membership inference, they reduce the reconstruction rate to ≤ 1% while reducing the classification accuracy by only 2% against model inversion attacks. This is the first attempt to defend model inversion and membership inference attacks using decentralized privacy protection. / Master of Science / Privacy attacks are threats posed to extract sensitive information about the data used to train machine learning models. As machine learning is used extensively for many applications, they have access to private information like financial records, medical history, etc depending on the application. It has been observed that machine learning models can leak the information they contain. As models tend to 'memorize' training data to some extent, even removing the data from the training set cannot prevent privacy leakage. As a result, the research community has focused its attention on developing defense techniques to prevent this information leakage. However, the existing defenses rely heavily on making alterations to the way a machine learning model is trained. This approach is termed as a model-centric approach wherein the model owner is responsible to make changes to the model algorithm to preserve data privacy. By doing this, the model performance is degraded while upholding data privacy. Our work introduces the first data-centric defense which provides the tools to protect the data to the data owner. We demonstrate the effectiveness of the proposed defense in providing protection while ensuring that the model performance is maintained to a great extent.

No description available.

This thesis examines how organisations within the IT software development industry manage risks associated with IT security and data privacy, with factors such as a growth in digitalisation and the Covid-19 pandemic. The research consists of four separate cases with interviewees in managerial positions in four different organisations.  The research shows the risks and challenges from an operational, financial, and reputational perspective. Development of the existing methods has been identified using cryptocurrencies as means to expose system vulnerabilities, an increase in monitoring and surveillance, which comes with considerations of follow-up and communication, along with the concept of moral hazards and their future implications. Furthermore, IT security organisations strive towards a risk tolerance approaching zero, as a result, discrepancies can occur between growth and risk. Considerations towards the compliance of data privacy must also be made, as new legislations take shape while being attentive to the stakeholders' changes in demands and expectations.  Contributions are made towards the field of risk management and IT security by taking a new era of digitalisation into consideration, giving the field an updated outlook for the future as the importance of data privacy and IT security is increasing. Therefore, the thesis provides valuable information that can be used as guidelines for organisations in this rapidly developing global environment.

Businesses analyze large datasets in order to extract valuable insights from the data. Unfortunately, most real datasets contain errors that need to be corrected before any analysis. Businesses can utilize various data cleaning systems and algorithms to automate the correction of data errors. Many systems correct the data errors by using information present within the dirty dataset itself. Some also incorporate user feedback in order to validate the quality of the suggested data corrections. However, users are not always available for feedback. Hence, some systems rely on clean data sources to help with the data cleaning process. This involves comparing records between the dirty dataset and the clean dataset in order to detect high quality fixes for the erroneous data. Every record in the dirty dataset is compared with every record in the clean dataset in order to find similar records. The values of the records in the clean dataset can be used to correct the values of the erroneous records in the dirty dataset. Realistically, comparing records across two datasets may not be possible due to privacy reasons. For example, there are laws to restrict the free movement of personal data. Additionally, different records within a dataset may have different privacy requirements. Existing data cleaning systems do not factor in these privacy requirements on the respective datasets. This motivates the need for privacy aware data cleaning systems. In this thesis, we examine the role of privacy in the data cleaning process. We present a novel data cleaning framework that supports the cooperation between the clean and the dirty datasets such that the clean dataset discloses a minimal amount of information and the dirty dataset uses this information to (maximally) clean its data. We investigate the tradeoff between information disclosure and data cleaning utility, modelling this tradeoff as a multi-objective optimization problem within our framework. We propose four optimization functions to solve our optimization problem. Finally, we perform extensive experiments on datasets containing up to 3 million records by varying parameters such as the error rate of the dataset, the size of the dataset, the number of constraints on the dataset, etc and measure the impact on accuracy and performance for those parameters. Our results demonstrate that disclosing a larger amount of information within the clean dataset helps in cleaning the dirty dataset to a larger extent. We find that with 80% information disclosure (relative to the weighted optimization function), we are able to achieve a precision of 91% and a recall of 85%. We also compare our algorithms against each other to discover which ones produce better data repairs and which ones take longer to find repairs. We incorporate ideas from Barone et al. into our framework and show that our approach is 30% faster, but 7% worse for precision. We conclude that our data cleaning framework can be applied to real-world scenarios where controlling the amount of information disclosed is important. / Thesis / Master of Computer Science (MCS) / Businesses analyze large datasets in order to extract valuable insights from the data. Unfortunately, most real datasets contain errors that need to be corrected before any analysis. Businesses can utilize various data cleaning systems and algorithms to automate the correction of data errors. Many systems correct the data errors by using information present within the dirty dataset itself. Some also incorporate user feedback in order to validate the quality of the suggested data corrections. However, users are not always available for feedback. Hence, some systems rely on clean data sources to help with the data cleaning process. This involves comparing records between the dirty dataset and the clean dataset in order to detect high quality fixes for the erroneous data. Every record in the dirty dataset is compared with every record in the clean dataset in order to find similar records. The values of the records in the clean dataset can be used to correct the values of the erroneous records in the dirty dataset. Realistically, comparing records across two datasets may not be possible due to privacy reasons. For example, there are laws to restrict the free movement of personal data. Additionally, different records within a dataset may have different privacy requirements. Existing data cleaning systems do not factor in these privacy requirements on the respective datasets. This motivates the need for privacy aware data cleaning systems. In this thesis, we examine the role of privacy in the data cleaning process. We present a novel data cleaning framework that supports the cooperation between the clean and the dirty datasets such that the clean dataset discloses a minimal amount of information and the dirty dataset uses this information to (maximally) clean its data. We investigate the tradeoff between information disclosure and data cleaning utility, modelling this tradeoff as a multi-objective optimization problem within our framework. We propose four optimization functions to solve our optimization problem. Finally, we perform extensive experiments on datasets containing up to 3 million records by varying parameters such as the error rate of the dataset, the size of the dataset, the number of constraints on the dataset, etc and measure the impact on accuracy and performance for those parameters. Our results demonstrate that disclosing a larger amount of information within the clean dataset helps in cleaning the dirty dataset to a larger extent. We find that with 80% information disclosure (relative to the weighted optimization function), we are able to achieve a precision of 91% and a recall of 85%. We also compare our algorithms against each other to discover which ones produce better data repairs and which ones take longer to find repairs. We incorporate ideas from Barone et al. into our framework and show that our approach is 30% faster, but 7% worse for precision. We conclude that our data cleaning framework can be applied to real-world scenarios where controlling the amount of information disclosed is important.