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Pri-View: Privacy-Preserving Views for Data Analysis and Publication
Publication . Costa, João Miguel Pereira; Preguiça, Nuno
Data is being generated and processed at an unprecedented scale. Statistical data analysis
is in high demand, with many organizations using it for a broad range of interests, from
researching to guiding business decisions. However, this massive generation of data raises
privacy concerns, as most of this data contains sensitive information about individuals.
In turn, several regulations have emerged to give people more control over their data,
such as the European General Data Protection Regulation.
For organizations, the challenge is how to analyze and publish data without compro-
mising an individual’s privacy. In the context of Relational Databases, they still lack
features for this, with solutions involving manually removing identifying information
from the data or only allowing certain aggregate queries. However, these solutions can
be susceptible to attacks and do not provide strong privacy guarantees.
In this thesis, we propose to explore a solution to address the challenge of privately
analyzing and publishing data on Relational Databases. To this end, we present a new
type of Views - privacy-preserving views - which allow for computing statistical aggre-
gations on data while preserving privacy. We focus our studies on Differential Privacy, a
recent mathematical definition of privacy, and explore how to turn common aggregation
functions into their private counterparts.
We present our solution in two parts. In the first part, we present a solution to create
privacy-preserving views for a specific database, namely PostgreSQL. In the second part,
we present the design and implementation of a database proxy, which supports any SQL
database and produces private statistical results. The experimental results show that our
proposed solutions can achieve balanced performance - views containing count functions
perform better than views containing other functions. They also show that both solutions
are capable of providing accurate privacy-preserving data for large databases and sample
sizes.
On Forgetting Relations in Relational Databases
Publication . Fonseca, Guilherme António Duarte; Leite, João; Gonçalves, Ricardo
Although not usually acknowledged as such, forgetting is a crucial aspect of human reasoning.
It allows us to deal with large amounts of information, pushing irrelevant details
out of our consciousness so that we can focus on the essential knowledge. Motivated
by its beneficial effect on the human brain, this operation has been emulated in many
formalisms in the field of Knowledge Representation and Reasoning, where several approaches
to forgetting have been proposed. In common, these support computer systems
dealing with inaccurate or excessive information without negatively affecting the remaining
knowledge. More recently, the General Data Protection Regulation’s ‘right to be
forgotten’ has given additional impetus to the study of this operation.
Surprisingly, forgetting has not yet been studied in relational databases, the most
widespread technology for knowledge representation. This is a serious drawback that
needs to be addressed, considering the prominence of databases in our society and the
relevance of the operation in numerous knowledge processing tasks.
In this dissertation, we take the first steps to tackle this need, proposing a theoretical
investigation of forgetting relations in relational databases. We start by introducing
an alternative formalisation of the relational model, which includes a novel notion of
equivalence between databases. Afterwards, we look further into the problem of forgetting.
We formally define the general concept of a relation forgetting operator and present
concrete operators, each aligned with a distinct view on the operation and thus with its
unique features. Moreover, we illustrate the operators with examples inspired by realistic
situations. Finally, we evaluate them. For that, we formalise in the form of properties
the requirements that guided the definition of the operators and prove that they satisfy
desirable properties. Ultimately, with this work, we motivate the importance of forgetting
in relational databases and lay the foundations for its study.
A Brief History of Updates of Answer-Set Programs
Publication . Leite, João; Slota, Martin; NOVALincs; DI - Departamento de Informática; Cambridge University Press
Over the last couple of decades, there has been a considerable effort devoted to the problem of updating logic programs under the stable model semantics (a.k.a. answer-set programs) or, in other words, the problem of characterising the result of bringing up-to-date a logic program when the world it describes changes. Whereas the state-of-the-art approaches are guided by the same basic intuitions and aspirations as belief updates in the context of classical logic, they build upon fundamentally different principles and methods, which have prevented a unifying framework that could embrace both belief and rule updates. In this paper, we will overview some of the main approaches and results related to answer-set programming updates, while pointing out some of the main challenges that research in this topic has faced.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
PTDC/CCI-INF/32219/2017