5,510 research outputs found
Opacity with Orwellian Observers and Intransitive Non-interference
Opacity is a general behavioural security scheme flexible enough to account
for several specific properties. Some secret set of behaviors of a system is
opaque if a passive attacker can never tell whether the observed behavior is a
secret one or not. Instead of considering the case of static observability
where the set of observable events is fixed off line or dynamic observability
where the set of observable events changes over time depending on the history
of the trace, we consider Orwellian partial observability where unobservable
events are not revealed unless a downgrading event occurs in the future of the
trace. We show how to verify that some regular secret is opaque for a regular
language L w.r.t. an Orwellian projection while it has been proved undecidable
even for a regular language L w.r.t. a general Orwellian observation function.
We finally illustrate relevancy of our results by proving the equivalence
between the opacity property of regular secrets w.r.t. Orwellian projection and
the intransitive non-interference property
Checking and Enforcing Security through Opacity in Healthcare Applications
The Internet of Things (IoT) is a paradigm that can tremendously
revolutionize health care thus benefiting both hospitals, doctors and patients.
In this context, protecting the IoT in health care against interference,
including service attacks and malwares, is challenging. Opacity is a
confidentiality property capturing a system's ability to keep a subset of its
behavior hidden from passive observers. In this work, we seek to introduce an
IoT-based heart attack detection system, that could be life-saving for patients
without risking their need for privacy through the verification and enforcement
of opacity. Our main contributions are the use of a tool to verify opacity in
three of its forms, so as to detect privacy leaks in our system. Furthermore,
we develop an efficient, Symbolic Observation Graph (SOG)-based algorithm for
enforcing opacity
Ionization state, excited populations and emission of impurities in dynamic finite density plasmas: I. The generalized collisional-radiative model for light elements
The paper presents an integrated view of the population structure and its role in establishing the ionization state of light elements in dynamic, finite density, laboratory and astrophysical plasmas. There are four main issues, the generalized collisional-radiative picture for metastables in dynamic plasmas with Maxwellian free electrons and its particularizing to light elements, the methods of bundling and projection for manipulating the population equations, the systematic production/use of state selective fundamental collision data in the metastable resolved picture to all levels for collisonal-radiative modelling and the delivery of appropriate derived coefficients for experiment analysis. The ions of carbon, oxygen and neon are used in illustration. The practical implementation of the methods described here is part of the ADAS Project
Probabilistic Opacity for Markov Decision Processes
Opacity is a generic security property, that has been defined on (non
probabilistic) transition systems and later on Markov chains with labels. For a
secret predicate, given as a subset of runs, and a function describing the view
of an external observer, the value of interest for opacity is a measure of the
set of runs disclosing the secret. We extend this definition to the richer
framework of Markov decision processes, where non deterministic choice is
combined with probabilistic transitions, and we study related decidability
problems with partial or complete observation hypotheses for the schedulers. We
prove that all questions are decidable with complete observation and
-regular secrets. With partial observation, we prove that all
quantitative questions are undecidable but the question whether a system is
almost surely non opaque becomes decidable for a restricted class of
-regular secrets, as well as for all -regular secrets under
finite-memory schedulers
Verification of Information Flow Properties under Rational Observation
Information flow properties express the capability for an agent to infer
information about secret behaviours of a partially observable system. In a
language-theoretic setting, where the system behaviour is described by a
language, we define the class of rational information flow properties (RIFP),
where observers are modeled by finite transducers, acting on languages in a
given family . This leads to a general decidability criterion for
the verification problem of RIFPs on , implying
PSPACE-completeness for this problem on regular languages. We show that most
trace-based information flow properties studied up to now are RIFPs, including
those related to selective declassification and conditional anonymity. As a
consequence, we retrieve several existing decidability results that were
obtained by ad-hoc proofs.Comment: 19 pages, 7 figures, version extended from AVOCS'201
Probabilistic Opacity in Refinement-Based Modeling
Given a probabilistic transition system (PTS) partially observed by
an attacker, and an -regular predicate over the traces of
, measuring the disclosure of the secret in means
computing the probability that an attacker who observes a run of can
ascertain that its trace belongs to . In the context of refinement, we
consider specifications given as Interval-valued Discrete Time Markov Chains
(IDTMCs), which are underspecified Markov chains where probabilities on edges
are only required to belong to intervals. Scheduling an IDTMC produces
a concrete implementation as a PTS and we define the worst case disclosure of
secret in as the maximal disclosure of over all
PTSs thus produced. We compute this value for a subclass of IDTMCs and we prove
that refinement can only improve the opacity of implementations
Quantitative Analysis of Opacity in Cloud Computing Systems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Federated cloud systems increase the reliability and reduce the cost of the computational support.
The resulting combination of secure private clouds and less secure public clouds, together with the fact that resources need to be located within different clouds, strongly affects the information flow security of the entire system. In this paper, the clouds as well as entities of a federated cloud system are
assigned security levels, and a probabilistic flow sensitive security model for a federated cloud system is proposed. Then the notion of opacity --- a notion capturing the security of information flow ---
of a cloud computing systems is introduced, and different variants of quantitative analysis of opacity are presented. As a result, one can track the information flow in a cloud system, and analyze the impact of different resource allocation strategies by quantifying the corresponding opacity characteristics
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