Lime: Data Lineage in the Malicious Environment

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

Adaptive Density Estimation for Generative Models

Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models. GANs have dramatically improved sample quality, but suffer from two drawbacks: (i) they mode-drop, i.e., do not cover the full support of the train data, and (ii) they do not allow for likelihood evaluations on held-out data. In contrast, likelihood-based training encourages models to cover the full support of the train data, but yields poorer samples. These mutual shortcomings can in principle be addressed by training generative latent variable models in a hybrid adversarial-likelihood manner. However, we show that commonly made parametric assumptions create a conflict between them, making successful hybrid models non trivial. As a solution, we propose to use deep invertible transformations in the latent variable decoder. This approach allows for likelihood computations in image space, is more efficient than fully invertible models, and can take full advantage of adversarial training. We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models, and improved likelihood scores

Hydraulic and thermal characterization of a family of thermo-hydraulic separators

Thermal-hydraulic separators (or hydraulic dispatchers) are flow collectors of relatively small size, connecting two or more hydraulic networks; they are mainly used to reduce the hydraulic interference of primary (e.g. heat supply) and secondary (e.g. heat consumer) circuits, thereby simplifying the system analysis, saving energy and operating complex hydraulic networks more safely. Hydraulic dispatchers are key components of modern district heating networks and of building water systems: nonetheless, little is known about their internal flow and temperature distribution and about their off-design performance. The transfer of thermal energy from the primary to the secondary circuit is governed by the secondary-to-primary flow rate ratio and affected by the turbulent mixing within the device. A simple thermal model is commonly used for design purposes: it disregards the actual flow and temperature pattern within the device and relates the inflow and outflow temperature by neglecting the mixing of supply and return streams. This simple approach is potentially inaccurate under certain operating conditions (e.g. relatively high flow rates) and deserves a validation study. Numerical simulations of flow and heat transfer are carried out, for a family of geometrically similar thermal-hydraulic separators under different operating condition. The reported numerical tests show that the aforementioned model is a reliable design tool under most operation conditions. Furthermore, it is verified that the device introduces a relatively modest pressure loss on the connected circuits, in particular when the flow rate ratio is close to one

Throughput Optimal Flow Allocation on Multiple Paths for Random Access Wireless Multi-hop Networks

In this paper we consider random access wireless multi-hop mesh networks with multi-packet reception capabilities where multiple flows are forwarded to the gateways through node disjoint paths. We address the issue of aggregate throughput-optimal flow rate allocation with bounded delay guarantees. We propose a distributed flow rate allocation scheme that formulates flow rate allocation as an optimization problem and derive the conditions for non-convexity for an illustrative topology. We also employ a simple model for the average aggregate throughput achieved by all flows that captures both intra- and inter-path interference. The proposed scheme is evaluated through NS-2 simulations. Our preliminary results are derived from a grid topology and show that the proposed flow allocation scheme slightly underestimates the average aggregate throughput observed in two simulated scenarios with two and three flows respectively. Moreover it achieves significantly higher average aggregate throughput than single path utilization in two different traffic scenarios examined.Comment: Accepted for publication at the 9th IEEE BROADBAND WIRELESS ACCESS WORKSHOP (BWA2013), IEEE Globecom 2013 Workshop