6,160 research outputs found
Oblivious Sensor Fusion via Secure Multi-Party Combinatorial Filter Evaluation
This thesis examines the problem of fusing data from several sensors, potentially distributed throughout an environment, in order to consolidate readings into a single coherent view. We consider the setting when sensor units do not wish others to know their specific sensor streams. Standard methods for handling this fusion make no guarantees about what a curious observer may learn. Motivated by applications where data sources may only choose to participate if given privacy guarantees, we introduce a fusion approach that limits what can be inferred. Our approach is to form an aggregate stream, oblivious to the underlying sensor data, and to evaluate a combinatorial filter on that stream. This is achieved via secure multi-party computational techniques built on cryptographic primitives, which we extend and apply to the problem of fusing discrete sensor signals. We prove that the extensions preserve security under the semi- honest adversary model. Though the approach enables several applications of potential interest, we specifically consider a target tracking case study as a running example. Finally, we also report on a basic, proof-of-concept implementation, demonstrating that it can operate in practice; which we report and analyze the (empirical) running times for components in the architecture, suggesting directions for future improvement
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
Multidomain Network Based on Programmable Networks: Security Architecture
This paper proposes a generic security architecture
designed for a multidomain and multiservice network
based on programmable networks. The multiservice
network allows users of an IP network to run
programmable services using programmable nodes
located in the architecture of the network. The
programmable nodes execute codes to process active
packets, which can carry user data and control
information. The multiservice network model defined
here considers the more pragmatic trends in
programmable networks. In this scenario, new security
risks that do not appear in traditional IP networks become
visible. These new risks are as a result of the execution of
code in the programmable nodes and the processing of the
active packets. The proposed security architecture is based
on symmetric cryptography in the critical process,
combined with an efficient manner of distributing the
symmetric keys. Another important contribution has been
to scale the security architecture to a multidomain
scenario in a single and efficient way.Publicad
Routes for breaching and protecting genetic privacy
We are entering the era of ubiquitous genetic information for research,
clinical care, and personal curiosity. Sharing these datasets is vital for
rapid progress in understanding the genetic basis of human diseases. However,
one growing concern is the ability to protect the genetic privacy of the data
originators. Here, we technically map threats to genetic privacy and discuss
potential mitigation strategies for privacy-preserving dissemination of genetic
data.Comment: Draft for comment
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