Garantia de privacidade na exploração de bases de dados distribuídas

Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid this disclosure is to ensure that all data that can be associated directly with an individual is removed from the original dataset. However, some studies have shown that simple anonymisation procedures can sometimes be reverted using specific patients’ characteristics, namely when the anonymisation is based on hidden key attributes. In this work, we propose a secure architecture to share information from distributed databases without compromising the subjects’ privacy. The work was initially focused on identifying techniques to link information between multiple data sources, in order to revert the anonymization procedures. In a second phase, we developed the methodology to perform queries over distributed databases was proposed. The architecture was validated using a standard data schema that is widely adopted in observational research studies.A garantia da anonimização de dados é atualmente um dos maiores desafios quando existe a necessidade de partilhar informações pessoais de carácter sensível. Apesar de ser um problema transversal a muitos domínios de aplicação, este torna-se mais crítico quando a anonimização envolve dados clinicos. Nestes casos, a abordagem mais comum para evitar a divulgação de dados, que possam ser associados diretamente a um indivíduo, consiste na remoção de atributos identificadores. No entanto, segundo a literatura, esta abordagem não oferece uma garantia total de anonimato, que pode ser quebrada através de ataques específicos que permitem a reidentificação dos sujeitos. Neste trabalho, é proposta uma arquitetura que permite partilhar dados armazenados em repositórios distribuídos, de forma segura e sem comprometer a privacidade. Numa primeira fase deste trabalho, foi feita uma análise de técnicas que permitam reverter os procedimentos de anonimização. Na fase seguinte, foi proposta uma metodologia que permite realizar pesquisas em bases de dados distribuídas, sem que o anonimato seja quebrado. Esta arquitetura foi validada sobre um esquema de base de dados relacional que é amplamente utilizado em estudos clínicos observacionais.Mestrado em Ciberseguranç

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Privacy-Preserving Design of Data Processing Systems in the Public Transport Context

The public transport network of a region inhabited by more than 4 million people is run by a complex interplay of public and private actors. Large amounts of data are generated by travellers, buying and using various forms of tickets and passes. Analysing the data is of paramount importance for the governance and sustainability of the system. This manuscript reports the early results of the privacy analysis which is being undertaken as part of the analysis of the clearing process in the Emilia-Romagna region, in Italy, which will compute the compensations for tickets bought from one operator and used with another. In the manuscript it is shown by means of examples that the clearing data may be used to violate various privacy aspects regarding users, as well as (technically equivalent) trade secrets regarding operators. The ensuing discussion has a twofold goal. First, it shows that after researching possible existing solutions, both by reviewing the literature on general privacy-preserving techniques, and by analysing similar scenarios that are being discussed in various cities across the world, the former are found exhibiting structural effectiveness deficiencies, while the latter are found of limited applicability, typically involving less demanding requirements. Second, it traces a research path towards a more effective approach to privacy-preserving data management in the specific context of public transport, both by refinement of current sanitization techniques and by application of the privacy by design approach. Available at: https://aisel.aisnet.org/pajais/vol7/iss4/4

The Challenges of Effectively Anonymizing Network Data

The availability of realistic network data plays a significant role in fostering collaboration and ensuring U.S. technical leadership in network security research. Unfortunately, a host of technical, legal, policy, and privacy issues limit the ability of operators to produce datasets for information security testing. In an effort to help overcome these limitations, several data collection efforts (e.g., CRAWDAD[14], PREDICT [34]) have been established in the past few years. The key principle used in all of these efforts to assure low-risk, high-value data is that of trace anonymization—the process of sanitizing data before release so that potentially sensitive information cannot be extracted

The impact of technology on data collection: Case studies in privacy and economics

Technological advancement can often act as a catalyst for scientific paradigm shifts. Today the ability to collect and process large amounts of data about individuals is arguably a paradigm-shift enabling technology in action. One manifestation of this technology within the sciences is the ability to study historically qualitative fields with a more granular quantitative lens than ever before. Despite the potential for this technology, wide-adoption is accompanied by some risks. In this thesis, I will present two case studies. The first, focuses on the impact of machine learning in a cheapest-wins motor insurance market by designing a competition-based data collection mechanism. Pricing models in the insurance industry are changing from statistical methods to machine learning. In this game, close to 2000 participants, acting as insurance companies, trained and submitted pricing models to compete for profit using real motor insurance policies --- with a roughly equal split between legacy and advanced models. With this trend towards machine learning in motion, preliminary analysis of the results suggest that future markets might realise cheaper prices for consumers. Additionally legacy models competing against modern algorithms, may experience a reduction in earning stability --- accelerating machine learning adoption. Overall, the results of this field experiment demonstrate the potential for digital competition-based studies of markets in the future. The second case studies the privacy risks of data collection technologies. Despite a large body of research in re-identification of anonymous data, the question remains: if a dataset was big enough, would records become anonymous by being "lost in the crowd"? Using 3 months of location data, we show that the risk of re-identification decreases slowly with dataset size. This risk is modelled and extrapolated to larger populations with 93% of people being uniquely identifiable using 4 points of auxiliary information among 60M people. These results show how the privacy of individuals is very unlikely to be preserved even in country-scale location datasets and that alternative paradigms of data sharing are still required.Open Acces