10,501 research outputs found
Privacy-Compatibility For General Utility Metrics
In this note, we present a complete characterization of the utility metrics
that allow for non-trivial differential privacy guarantees
Constructing elastic distinguishability metrics for location privacy
With the increasing popularity of hand-held devices, location-based
applications and services have access to accurate and real-time location
information, raising serious privacy concerns for their users. The recently
introduced notion of geo-indistinguishability tries to address this problem by
adapting the well-known concept of differential privacy to the area of
location-based systems. Although geo-indistinguishability presents various
appealing aspects, it has the problem of treating space in a uniform way,
imposing the addition of the same amount of noise everywhere on the map. In
this paper we propose a novel elastic distinguishability metric that warps the
geometrical distance, capturing the different degrees of density of each area.
As a consequence, the obtained mechanism adapts the level of noise while
achieving the same degree of privacy everywhere. We also show how such an
elastic metric can easily incorporate the concept of a "geographic fence" that
is commonly employed to protect the highly recurrent locations of a user, such
as his home or work. We perform an extensive evaluation of our technique by
building an elastic metric for Paris' wide metropolitan area, using semantic
information from the OpenStreetMap database. We compare the resulting mechanism
against the Planar Laplace mechanism satisfying standard
geo-indistinguishability, using two real-world datasets from the Gowalla and
Brightkite location-based social networks. The results show that the elastic
mechanism adapts well to the semantics of each area, adjusting the noise as we
move outside the city center, hence offering better overall privacy
Notes on Cloud computing principles
This letter provides a review of fundamental distributed systems and economic
Cloud computing principles. These principles are frequently deployed in their
respective fields, but their inter-dependencies are often neglected. Given that
Cloud Computing first and foremost is a new business model, a new model to sell
computational resources, the understanding of these concepts is facilitated by
treating them in unison. Here, we review some of the most important concepts
and how they relate to each other
When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy
In recent years, it has become easy to obtain location information quite
precisely. However, the acquisition of such information has risks such as
individual identification and leakage of sensitive information, so it is
necessary to protect the privacy of location information. For this purpose,
people should know their location privacy preferences, that is, whether or not
he/she can release location information at each place and time. However, it is
not easy for each user to make such decisions and it is troublesome to set the
privacy preference at each time. Therefore, we propose a method to recommend
location privacy preferences for decision making. Comparing to existing method,
our method can improve the accuracy of recommendation by using matrix
factorization and preserve privacy strictly by local differential privacy,
whereas the existing method does not achieve formal privacy guarantee. In
addition, we found the best granularity of a location privacy preference, that
is, how to express the information in location privacy protection. To evaluate
and verify the utility of our method, we have integrated two existing datasets
to create a rich information in term of user number. From the results of the
evaluation using this dataset, we confirmed that our method can predict
location privacy preferences accurately and that it provides a suitable method
to define the location privacy preference
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
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