Coded Computation Against Processing Delays for Virtualized Cloud-Based Channel Decoding

The uplink of a cloud radio access network architecture is studied in which decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the trade-off between average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability.Comment: 11 pages and 12 figures, Submitte

Coding against stragglers in distributed computation scenarios

Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially. The huge amount of data needs to be transferred and stored with extremely high reliability. The concept of coded computing , or a distributed computing paradigm that utilizes coding theory to smartly inject and leverage data/computation redundancy into distributed computing systems, mitigates the fundamental performance bottlenecks for running large-scale data analytics. In this dissertation, a distributed computing framework, first for input files distributedly stored on the uplink of a cloud radio access network architecture, is studied. It focuses on that decoding at the cloud takes place via network function virtualization on commercial off-the-shelf servers. In order to mitigate the impact of straggling decoders in this platform, a novel coding strategy is proposed, whereby the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. Transmission of a single frame is considered first, and upper bounds on the resulting frame unavailability probability as a function of the decoding latency are derived by assuming a binary symmetric channel for uplink communications. Then, the analysis is extended to account for random frame arrival times. In this case, the trade-off between an average decoding latency and the frame error rate is studied for two different queuing policies, whereby the servers carry out per-frame decoding or continuous decoding, respectively. Numerical examples demonstrate that the bounds are useful tools for code design and that coding is instrumental in obtaining a desirable compromise between decoding latency and reliability. In the second part of this dissertation large matrix multiplications are considered which are central to large-scale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, and the total amount of data to be downloaded from the workers. In addition to exact recovery requirements, security and privacy constraints on the data matrices are imposed, and the recovery threshold as a function of the communication load is studied. First, it is assumed that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, a novel class of secure codes is introduced, referred to as secure generalized PolyDot codes, that generalize state-of-the-art non-secure codes for matrix multiplication. Secure generalized PolyDot codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. Then, a connection between secure matrix multiplication and private information retrieval is studied. It is assumed that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, a variant of generalized PolyDot codes is presented that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers

Antibacterial and anticancer activities of silver nanoparticles fabricated by the Artemisia scoparia extract against lung cancer cell line (A549)

Background: In recent years, due to an increase in prevalence of cancer, there is a need to find new ways to control this disease. The aim of this study was to evaluate antibacterial and anti-cancer activities of silver nanoparticles (AgNPs) fabricated by Artemisia scoparia extract gainst lung cancer cell line (A549). Materials and Methods: In this experimental study, AgNPs was synthesized and ccharacterization of fabricated AgNPs was performed by scanning and transmission electron microscopy (SEM, TEM) methods. Subsequently, antibacterial activity of synthesized AgNPs was determined using the minimum inhibitory concentration (MIC) method. Moreover, cell toxicity of AgNPs against A549 cell line in 3.125-100 µg/mL concentrations was performed using the MTT method. The gene expression of Bax and Bcl2 was measured using the real-time PCR. Results: The results of SEM and TEM showed that the synthesized AgNPs had 33.40 nm average size. The MIC results demonstrated that the AgNPs had a significant antibacterial activity. In addition, the MTT results showed that AgNPs had dose-dependent cell toxicity. The real-time PCR results revealed that the Bax and Bcl2 gene expression were up-regulated (2.34±0.34) and down-regulated (0.43±0.61), respectively (P<0.05). Conclusion: According to the results of this study, the AgNPs had antibacterial and anticancer activities and it can be used as a drug candidate

Distributed and Private Coded Matrix Computation with Flexible Communication Load

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. For user-driven tasks these operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold and communication load. In this paper we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.Comment: 8 pages, 6 figures, submitted to 2019 IEEE International Symposium on Information Theory (ISIT

Mitigating Autonomous Vehicle GPS Spoofing Attacks through Scene Text Observations

This paper investigates both from an empirical and a systems-based perspective, how surrounding textual information can be leveraged towards the mitigation of Autonomous Vehicle (AV) and self-driving cars Global Positioning System (GPS) signal spoofing attacks. The paper presents and proposes methods of how AVs and self-driving cars can extract, as they travel along a trajectory, surrounding textual information through machine-learning based Scene Text Recognition (STR). The paper researches and proposes geospatial models which can be applied to the extracted textual information in order to build a text-based geolocation system for the purposes of validating the received GPS signal. The ultimate contribution of the paper is to lay the groundwork towards enhancing the Cybersecurity of the current and future Autonomous Vehicle and self-driving car ecosystem by addressing its Achilles heel, namely insecure and inaccurate geolocation due to GPS spoofing attacks