Secure distributed matrix computation with discrete fourier transform

We consider the problem of secure distributed matrix computation (SDMC), where a user queries a function of data matrices generated at distributed source nodes. We assume the availability of N honest but curious computation servers, which are connected to the sources, the user, and each other through orthogonal and reliable communication links. Our goal is to minimize the amount of data that must be transmitted from the sources to the servers, called the upload cost, while guaranteeing that no T colluding servers can learn any information about the source matrices, and the user cannot learn any information beyond the computation result. We first focus on secure distributed matrix multiplication (SDMM), considering two matrices, and propose a novel polynomial coding scheme using the properties of finite field discrete Fourier transform, which achieves an upload cost significantly lower than the existing results in the literature. We then generalize the proposed scheme to include straggler mitigation, and to the multiplication of multiple matrices while keeping the input matrices, the intermediate computation results, as well as the final result secure against any T colluding servers. We also consider a special case, called computation with own data, where the data matrices used for computation belong to the user. In this case, we drop the security requirement against the user, and show that the proposed scheme achieves the minimal upload cost. We then propose methods for performing other common matrix computations securely on distributed servers, including changing the parameters of secret sharing, matrix transpose, matrix exponentiation, solving a linear system, and matrix inversion, which are then used to show how arbitrary matrix polynomials can be computed securely on distributed servers using the proposed procedur

Coded caching in a multi-server system with random topology

Cache-aided content delivery is studied in a multi-server system with P servers and K users, each equipped with a local cache memory. In the delivery phase, each user connects randomly to any ρ out of P servers. Thanks to the availability of multiple servers, which model small-cell base stations (SBSs), demands can be satisfied with reduced storage capacity at each server and reduced delivery rate per server; however, this also leads to reduced multicasting opportunities compared to the single-server scenario. A joint storage and proactive caching scheme is proposed, which exploits coded storage across the servers, uncoded cache placement at the users, and coded delivery. The delivery latency is studied for both successive and parallel transmissions from the servers. It is shown that, with successive transmissions the achievable average delivery latency is comparable to the one achieved in the single-server scenario, while the gap between the two depends on ρ, the available redundancy across the servers, and can be reduced by increasing the storage capacity at the SBSs. The optimality of the proposed scheme with uncoded cache placement and MDS-coded server storage is also proved for successive transmissions

Functional broadcast repair of multiple partial failures in wireless distributed storage systems

We consider a distributed storage system with n nodes, where a user can recover the stored file from any k nodes, and study the problem of repairing r partially failed nodes. We consider broadcast repair , that is, d surviving nodes transmit broadcast messages on an error-free wireless channel to the r nodes being repaired, which are then used, together with the surviving data in the local memories of the failed nodes, to recover the lost content. First, we derive the trade-off between the storage capacity and the repair bandwidth for partial repair of multiple failed nodes, based on the cut-set bound for information flow graphs. It is shown that utilizing the broadcast nature of the wireless medium and the surviving contents at the partially failed nodes reduces the repair bandwidth per node significantly. Then, we list a set of invariant conditions that are sufficient for a functional repair code to be feasible. We further propose a scheme for functional repair of multiple failed nodes that satisfies the invariant conditions with high probability, and its extension to the repair of partial failures. The performance of the proposed scheme meets the cut-set bound on all the points on the trade-off curve for all admissible parameters when k is divisible by r , while employing linear subpacketization, which is an important practical consideration in the design of distributed storage codes. Unlike random linear codes, which are conventionally used for functional repair of failed nodes, the proposed repair scheme has lower overhead, lower input-output cost, and lower computational complexity during repair

Neural Distributed Image Compression Using Common Information

We present a novel deep neural network (DNN) architecture for compressing an image when a correlated image is available as side information only at the decoder, a special case of the well-known distributed source coding (DSC) problem in information theory. In particular, we consider a pair of stereo images, which generally have high correlation with each other due to overlapping fields of view, and assume that one image of the pair is to be compressed and transmitted, while the other image is available only at the decoder. In the proposed architecture, the encoder maps the input image to a latent space, quantizes the latent representation, and compresses it using entropy coding. The decoder is trained to extract the common information between the input image and the correlated image, using only the latter. The received latent representation and the locally generated common information are passed through a decoder network to obtain an enhanced reconstruction of the input image. The common information provides a succinct representation of the relevant information at the receiver. We train and demonstrate the effectiveness of the proposed approach on the KITTI and Cityscape datasets of stereo image pairs. Our results show that the proposed architecture is capable of exploiting the decoder-only side information, and outperforms previous work on stereo image compression with decoder side information

Neural distributed image compression with cross-attention feature alignment

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider a pair of stereo images, which have overlapping fields of view, and are captured by a synchronized and calibrated pair of cameras as correlated image sources. In previously proposed methods, the encoder transforms the input image to a latent representation using a deep neural network, and compresses the quantized latent representation losslessly using entropy coding. The decoder decodes the entropy-coded quantized latent representation, and reconstructs the input image using this representation and the available side information. In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information. We argue that aligning the correlated patches in the feature maps allows better utilization of the side information. We empirically demonstrate the competitiveness of the proposed algorithm on KITTI and Cityscape datasets of stereo image pairs. Our experimental results show that the proposed architecture is able to exploit the decoder-only side information in a more efficient manner compared to previous works

Regional Differences in Economic Impacts of Power Outages in Finland

Estimating the worth of continuity of electricity supply is of interest to industry, governments, regulators and the research community. There are numerous methods to calculate the Customer Interruption Costs (CICs). Each method has its advantages and disadvantages. We approach the problem from the Distribution System Operator (DSO) point of view and employ two existing analytical models. One model is used by the Finnish Energy Market Authority and the second one was proposed by some of the authors in a previous study. Our model offers a simple and straightforward methodology which can provide credible and objective estimations utilizing only publicly available analytical data. We made use of cost and reliability indices data of 78 DSOs in Finland from the 2016. In addition to cost estimations, we highlight regional differences in CIC estimations in different parts of Finland and provide a critical overview of the existing standard customer compensation scheme in Finland

Shadow Pricing of Electric Power Interruptions for Distribution System Operators in Finland

Increasing distributed generation and intermittency, along with the increasing frequency of extreme weather events, pose a serious challenge supply security in the electric power sector. Understanding the costs of interruption is vital for enhancing power system infrastructure and planning the distribution grid. Customer rights and demand response are additional reasons to study the value of power reliability. We make use of the directional distance function and shadow pricing method for a case study from Finland with the aim of calculating the cost of one minute of power interruption from the perspective of the distribution network operator. The sample consists of 78 distribution network operators from Finland based on cost and network information between 2013 and 2015

Functional broadcast repair of multiple partial failures in wireless distributed storage systems

We consider a distributed storage system with n nodes, where a user can recover the stored file from any k nodes, and study the problem of repairing r partially failed nodes. We consider broadcast repair , that is, d surviving nodes transmit broadcast messages on an error-free wireless channel to the r nodes being repaired, which are then used, together with the surviving data in the local memories of the failed nodes, to recover the lost content. First, we derive the trade-off between the storage capacity and the repair bandwidth for partial repair of multiple failed nodes, based on the cut-set bound for information flow graphs. It is shown that utilizing the broadcast nature of the wireless medium and the surviving contents at the partially failed nodes reduces the repair bandwidth per node significantly. Then, we list a set of invariant conditions that are sufficient for a functional repair code to be feasible. We further propose a scheme for functional repair of multiple failed nodes that satisfies the invariant conditions with high probability, and its extension to the repair of partial failures. The performance of the proposed scheme meets the cut-set bound on all the points on the trade-off curve for all admissible parameters when k is divisible by r , while employing linear subpacketization, which is an important practical consideration in the design of distributed storage codes. Unlike random linear codes, which are conventionally used for functional repair of failed nodes, the proposed repair scheme has lower overhead, lower input-output cost, and lower computational complexity during repair

3D Printed Polycaprolactone/Gelatin/Bacterial Cellulose/Hydroxyapatite Composite Scaffold for Bone Tissue Engineering

Three-dimensional (3D) printing application is a promising method for bone tissue engineering. For enhanced bone tissue regeneration, it is essential to have printable composite materials with appealing properties such as construct porous, mechanical strength, thermal properties, controlled degradation rates, and the presence of bioactive materials. In this study, polycaprolactone (PCL), gelatin (GEL), bacterial cellulose (BC), and different hydroxyapatite (HA) concentrations were used to fabricate a novel PCL/GEL/BC/HA composite scaffold using 3D printing method for bone tissue engineering applications. Pore structure, mechanical, thermal, and chemical analyses were evaluated. 3D scaffolds with an ideal pore size (~300 µm) for use in bone tissue engineering were generated. The addition of both bacterial cellulose (BC) and hydroxyapatite (HA) into PCL/GEL scaffold increased cell proliferation and attachment. PCL/GEL/BC/HA composite scaffolds provide a potential for bone tissue engineering applications

Kinetic Release Studies of Antibiotic Patches for Local Transdermal Delivery.

This study investigates the usage of electrohydrodynamic (EHD)-3D printing for the fabrication of bacterial cellulose (BC)/polycaprolactone (PCL) patches loaded with different antibiotics (amoxicillin (AMX), ampicillin (AMP), and kanamycin (KAN)) for transdermal delivery. The composite patches demonstrated facilitated drug loading and encapsulation efficiency of drugs along with extended drug release profiles. Release curves were also subjected to model fitting, and it was found that drug release was optimally adapted to the Higuchi square root model for each drug. They performed a time-dependent and diffusion-controlled release from the patches and followed Fick's diffusion law by the Korsmeyer-Peppas energy law equation. Moreover, produced patches demonstrated excellent antimicrobial activity against Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) strains, so they could be helpful in the treatment of chronic infectious lesions during wound closures. As different tests have confirmed, various types of antibiotics could be loaded and successfully released regardless of their types from produced BC/PCL patches. This study could breathe life into the production of antibiotic patches for local transdermal applications in wound dressing studies and improve the quality of life of patients