3,765 research outputs found
Garantia de privacidade na exploração de bases de dados distribuĂdas
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
sensĂvel. Apesar de ser um problema transversal a muitos domĂnios de
aplicação, este torna-se mais crĂtico 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 indivĂduo, consiste
na remoção de atributos identificadores. No entanto, segundo a literatura,
esta abordagem nĂŁo oferece uma garantia total de anonimato, que pode ser
quebrada atravĂ©s de ataques especĂficos que permitem a reidentificação dos
sujeitos.
Neste trabalho, Ă© proposta uma arquitetura que permite partilhar dados
armazenados em repositĂłrios distribuĂdos, 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 distribuĂdas, sem que o anonimato seja quebrado. Esta
arquitetura foi validada sobre um esquema de base de dados relacional que
Ă© amplamente utilizado em estudos clĂnicos observacionais.Mestrado em Ciberseguranç
Location privacy policy management system
The advance in wireless communication and positioning systems has permitted development of a large variety of location-based services that, for example, can help people easily locate family members or find nearest gas station or restaurant. As location-based services become more and more popular, concerns are growing about the misuse of location information by malicious parties. In order to preserve location privacy, many efforts have been devoted to preventing service providers from determining users\u27 exact locations. Few works have sought to help users manage their privacy preferences; however management of privacy is an important issue in real applications. This work developed an easy-to-use location privacy management system. Specifically, it defines a succinct yet expressive location privacy policy constructs that can be easily understood by ordinary users. The system provides various policy management functions including policy composition, policy conflict detection, and policy recommendation. Policy composition allows users to insert and delete policies. Policy conflict detection will automatically check conflict among policies whenever there is any change. The policy recommendation system will generate recommended policies based on users\u27 basic requirements in order to reduce users\u27 burden. A system prototype has been implemented and evaluated in terms of both efficiency and effectiveness --Abstract, page iii
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward
Trajectory Privacy Preservation and Lightweight Blockchain Techniques for Mobility-Centric IoT
Various research efforts have been undertaken to solve the problem of trajectory privacy preservation in the Internet of Things (IoT) of resource-constrained mobile devices. Most attempts at resolving the problem have focused on the centralized model of IoT, which either impose high delay or fail against a privacy-invading attack with long-term trajectory observation. These proposed solutions also fail to guarantee location privacy for trajectories with both geo-tagged and non-geo-tagged data, since they are designed for geo-tagged trajectories only. While a few blockchain-based techniques have been suggested for preserving trajectory privacy in decentralized model of IoT, they require large storage capacity on resource-constrained devices and can only provide conditional privacy when a set of authorities governs the blockchain. This dissertation addresses these challenges to develop efficient trajectory privacy-preservation and lightweight blockchain techniques for mobility-centric IoT.
We develop a pruning-based technique by quantifying the relationship between trajectory privacy and delay for real-time geo-tagged queries. This technique yields higher trajectory privacy with a reduced delay than contemporary techniques while preventing a long-term observation attack. We extend our study with the consideration of the presence of non-geo-tagged data in a trajectory. We design an attack model to show the spatiotemporal correlation between the geo-tagged and non-geo-tagged data which undermines the privacy guarantee of existing techniques. In response, we propose a methodology that considers the spatial distribution of the data in trajectory privacy-preservation and improves existing solutions, in privacy and usability.
With respect to blockchain, we design and implement one of the first blockchain storage management techniques utilizing the mobility of the devices. This technique reduces the required storage space of a blockchain and makes it lightweight for resource-constrained mobile devices. To address the trajectory privacy challenges in an authority-based blockchain under the short-range communication constraints of the devices, we introduce a silence-based one of the first technique to establish a balance between trajectory privacy and blockchain utility.
The designed trajectory privacy- preservation techniques we established are light- weight and do not require an intermediary to guarantee trajectory privacy, thereby providing practical and efficient solution for different mobility-centric IoT, such as mobile crowdsensing and Internet of Vehicles
Contributions to the privacy provisioning for federated identity management platforms
Identity information, personal data and user’s profiles are key assets for organizations
and companies by becoming the use of identity management (IdM) infrastructures a prerequisite
for most companies, since IdM systems allow them to perform their business
transactions by sharing information and customizing services for several purposes in more
efficient and effective ways.
Due to the importance of the identity management paradigm, a lot of work has been done
so far resulting in a set of standards and specifications. According to them, under the
umbrella of the IdM paradigm a person’s digital identity can be shared, linked and reused
across different domains by allowing users simple session management, etc. In this way,
users’ information is widely collected and distributed to offer new added value services
and to enhance availability. Whereas these new services have a positive impact on users’
life, they also bring privacy problems.
To manage users’ personal data, while protecting their privacy, IdM systems are the ideal
target where to deploy privacy solutions, since they handle users’ attribute exchange.
Nevertheless, current IdM models and specifications do not sufficiently address comprehensive
privacy mechanisms or guidelines, which enable users to better control over the
use, divulging and revocation of their online identities. These are essential aspects, specially
in sensitive environments where incorrect and unsecured management of user’s data
may lead to attacks, privacy breaches, identity misuse or frauds.
Nowadays there are several approaches to IdM that have benefits and shortcomings, from
the privacy perspective.
In this thesis, the main goal is contributing to the privacy provisioning for federated
identity management platforms. And for this purpose, we propose a generic architecture
that extends current federation IdM systems. We have mainly focused our contributions
on health care environments, given their particularly sensitive nature. The two main
pillars of the proposed architecture, are the introduction of a selective privacy-enhanced
user profile management model and flexibility in revocation consent by incorporating an
event-based hybrid IdM approach, which enables to replace time constraints and explicit
revocation by activating and deactivating authorization rights according to events. The
combination of both models enables to deal with both online and offline scenarios, as well
as to empower the user role, by letting her to bring together identity information from
different sources.
Regarding user’s consent revocation, we propose an implicit revocation consent mechanism
based on events, that empowers a new concept, the sleepyhead credentials, which
is issued only once and would be used any time. Moreover, we integrate this concept
in IdM systems supporting a delegation protocol and we contribute with the definition
of mathematical model to determine event arrivals to the IdM system and how they are
managed to the corresponding entities, as well as its integration with the most widely
deployed specification, i.e., Security Assertion Markup Language (SAML).
In regard to user profile management, we define a privacy-awareness user profile management
model to provide efficient selective information disclosure. With this contribution a
service provider would be able to accesses the specific personal information without being
able to inspect any other details and keeping user control of her data by controlling
who can access. The structure that we consider for the user profile storage is based on
extensions of Merkle trees allowing for hash combining that would minimize the need of
individual verification of elements along a path. An algorithm for sorting the tree as we
envision frequently accessed attributes to be closer to the root (minimizing the access’
time) is also provided.
Formal validation of the above mentioned ideas has been carried out through simulations
and the development of prototypes. Besides, dissemination activities were performed in
projects, journals and conferences.Programa Oficial de Doctorado en IngenierĂa TelemáticaPresidente: MarĂa Celeste Campo Vázquez.- Secretario: MarĂa Francisca Hinarejos Campos.- Vocal: Ă“scar Esparza MartĂ
Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping
Big data collection practices using Internet of Things (IoT) pervasive
technologies are often privacy-intrusive and result in surveillance, profiling,
and discriminatory actions over citizens that in turn undermine the
participation of citizens to the development of sustainable smart cities.
Nevertheless, real-time data analytics and aggregate information from IoT
devices open up tremendous opportunities for managing smart city
infrastructures. The privacy-enhancing aggregation of distributed sensor data,
such as residential energy consumption or traffic information, is the research
focus of this paper. Citizens have the option to choose their privacy level by
reducing the quality of the shared data at a cost of a lower accuracy in data
analytics services. A baseline scenario is considered in which IoT sensor data
are shared directly with an untrustworthy central aggregator. A grouping
mechanism is introduced that improves privacy by sharing data aggregated first
at a group level compared as opposed to sharing data directly to the central
aggregator. Group-level aggregation obfuscates sensor data of individuals, in a
similar fashion as differential privacy and homomorphic encryption schemes,
thus inference of privacy-sensitive information from single sensors becomes
computationally harder compared to the baseline scenario. The proposed system
is evaluated using real-world data from two smart city pilot projects. Privacy
under grouping increases, while preserving the accuracy of the baseline
scenario. Intra-group influences of privacy by one group member on the other
ones are measured and fairness on privacy is found to be maximized between
group members with similar privacy choices. Several grouping strategies are
compared. Grouping by proximity of privacy choices provides the highest privacy
gains. The implications of the strategy on the design of incentives mechanisms
are discussed
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