225,113 research outputs found
The Embedding Capacity of Information Flows Under Renewal Traffic
Given two independent point processes and a certain rule for matching points
between them, what is the fraction of matched points over infinitely long
streams? In many application contexts, e.g., secure networking, a meaningful
matching rule is that of a maximum causal delay, and the problem is related to
embedding a flow of packets in cover traffic such that no traffic analysis can
detect it. We study the best undetectable embedding policy and the
corresponding maximum flow rate ---that we call the embedding capacity--- under
the assumption that the cover traffic can be modeled as arbitrary renewal
processes. We find that computing the embedding capacity requires the inversion
of very structured linear systems that, for a broad range of renewal models
encountered in practice, admits a fully analytical expression in terms of the
renewal function of the processes. Our main theoretical contribution is a
simple closed form of such relationship. This result enables us to explore
properties of the embedding capacity, obtaining closed-form solutions for
selected distribution families and a suite of sufficient conditions on the
capacity ordering. We evaluate our solution on real network traces, which shows
a noticeable match for tight delay constraints. A gap between the predicted and
the actual embedding capacities appears for looser constraints, and further
investigation reveals that it is caused by inaccuracy of the renewal traffic
model rather than of the solution itself.Comment: Sumbitted to IEEE Trans. on Information Theory on March 10, 201
Beyond Good and Evil: Formalizing the Security Guarantees of Compartmentalizing Compilation
Compartmentalization is good security-engineering practice. By breaking a
large software system into mutually distrustful components that run with
minimal privileges, restricting their interactions to conform to well-defined
interfaces, we can limit the damage caused by low-level attacks such as
control-flow hijacking. When used to defend against such attacks,
compartmentalization is often implemented cooperatively by a compiler and a
low-level compartmentalization mechanism. However, the formal guarantees
provided by such compartmentalizing compilation have seen surprisingly little
investigation.
We propose a new security property, secure compartmentalizing compilation
(SCC), that formally characterizes the guarantees provided by
compartmentalizing compilation and clarifies its attacker model. We reconstruct
our property by starting from the well-established notion of fully abstract
compilation, then identifying and lifting three important limitations that make
standard full abstraction unsuitable for compartmentalization. The connection
to full abstraction allows us to prove SCC by adapting established proof
techniques; we illustrate this with a compiler from a simple unsafe imperative
language with procedures to a compartmentalized abstract machine.Comment: Nit
Compositional closure for Bayes Risk in probabilistic noninterference
We give a sequential model for noninterference security including probability
(but not demonic choice), thus supporting reasoning about the likelihood that
high-security values might be revealed by observations of low-security
activity. Our novel methodological contribution is the definition of a
refinement order and its use to compare security measures between
specifications and (their supposed) implementations. This contrasts with the
more common practice of evaluating the security of individual programs in
isolation.
The appropriateness of our model and order is supported by our showing that
our refinement order is the greatest compositional relation --the compositional
closure-- with respect to our semantics and an "elementary" order based on
Bayes Risk --- a security measure already in widespread use. We also relate
refinement to other measures such as Shannon Entropy.
By applying the approach to a non-trivial example, the anonymous-majority
Three-Judges protocol, we demonstrate by example that correctness arguments can
be simplified by the sort of layered developments --through levels of
increasing detail-- that are allowed and encouraged by compositional semantics
CamFlow: Managed Data-sharing for Cloud Services
A model of cloud services is emerging whereby a few trusted providers manage
the underlying hardware and communications whereas many companies build on this
infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS
applications. From the start, strong isolation between cloud tenants was seen
to be of paramount importance, provided first by virtual machines (VM) and
later by containers, which share the operating system (OS) kernel. Increasingly
it is the case that applications also require facilities to effect isolation
and protection of data managed by those applications. They also require
flexible data sharing with other applications, often across the traditional
cloud-isolation boundaries; for example, when government provides many related
services for its citizens on a common platform. Similar considerations apply to
the end-users of applications. But in particular, the incorporation of cloud
services within `Internet of Things' architectures is driving the requirements
for both protection and cross-application data sharing.
These concerns relate to the management of data. Traditional access control
is application and principal/role specific, applied at policy enforcement
points, after which there is no subsequent control over where data flows; a
crucial issue once data has left its owner's control by cloud-hosted
applications and within cloud-services. Information Flow Control (IFC), in
addition, offers system-wide, end-to-end, flow control based on the properties
of the data. We discuss the potential of cloud-deployed IFC for enforcing
owners' dataflow policy with regard to protection and sharing, as well as
safeguarding against malicious or buggy software. In addition, the audit log
associated with IFC provides transparency, giving configurable system-wide
visibility over data flows. [...]Comment: 14 pages, 8 figure
Design and Implementation of S-MARKS: A Secure Middleware for Pervasive Computing Applications
As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module
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