Synthesis of opaque systems with static and dynamic masks

International audienceOpacity is a security property formalizing the absence of secret information leakage and we address in this paper the problem of synthesizing opaque systems. A secret predicate S over the runs of a system G is opaque to an external user having partial observability over G, if s/he can never infer from the observation of a run of G that the run belongs to S. We choose to control the observability of events by adding a device, called a mask, between the system G and the users. We first investigate the case of static partial observability where the set of events the user can observe is fixed a priori by a static mask. In this context, we show that checking whether a system is opaque is PSPACE-complete, which implies that computing an optimal static mask ensuring opacity is also a PSPACE-complete problem. Next, we introduce dynamic partial observability where the set of events the user can observe changes over time and is chosen by a dynamic mask.We show how to check that a system is opaque w.r.t. to a dynamic mask and also address the corresponding synthesis problem: given a system G and secret states S, compute the set of dynamic masks under which S is opaque. Our main result is that the set of such masks can be finitely represented and can be computed in EXPTIME and this is a lower bound. Finally we also address the problem of computing an optimal mask

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

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 $\mathcal{L}$. This leads to a general decidability criterion for the verification problem of RIFPs on $\mathcal{L}$, 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

Layered 3D: tomographic image synthesis for attenuation-based light field and high dynamic range displays

We develop tomographic techniques for image synthesis on displays composed of compact volumes of light-attenuating material. Such volumetric attenuators recreate a 4D light field or high-contrast 2D image when illuminated by a uniform backlight. Since arbitrary oblique views may be inconsistent with any single attenuator, iterative tomographic reconstruction minimizes the difference between the emitted and target light fields, subject to physical constraints on attenuation. As multi-layer generalizations of conventional parallax barriers, such displays are shown, both by theory and experiment, to exceed the performance of existing dual-layer architectures. For 3D display, spatial resolution, depth of field, and brightness are increased, compared to parallax barriers. For a plane at a fixed depth, our optimization also allows optimal construction of high dynamic range displays, confirming existing heuristics and providing the first extension to multiple, disjoint layers. We conclude by demonstrating the benefits and limitations of attenuation-based light field displays using an inexpensive fabrication method: separating multiple printed transparencies with acrylic sheets.Dolby Laboratories Inc.Samsung ElectronicsAlfred P. Sloan Foundatio

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 $\omega$-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 $\omega$-regular secrets, as well as for all $\omega$-regular secrets under finite-memory schedulers

Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model

Capturing general deforming scenes is crucial for many computer graphics andvision applications, and it is especially challenging when only a monocular RGBvideo of the scene is available. Competing methods assume dense point tracks,3D templates, large-scale training datasets, or only capture small-scaledeformations. In contrast to those, our method, Ub4D, makes none of theseassumptions while outperforming the previous state of the art in challengingscenarios. Our technique includes two new, in the context of non-rigid 3Dreconstruction, components, i.e., 1) A coordinate-based and implicit neuralrepresentation for non-rigid scenes, which enables an unbiased reconstructionof dynamic scenes, and 2) A novel dynamic scene flow loss, which enables thereconstruction of larger deformations. Results on our new dataset, which willbe made publicly available, demonstrate the clear improvement over the state ofthe art in terms of surface reconstruction accuracy and robustness to largedeformations. Visit the project page https://4dqv.mpi-inf.mpg.de/Ub4D/.<br

FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic Neural Radiance Fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. Furthermore, we show an augmentation of the training data that relaxes the periodicity assumption of the compression. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes