A survey on computation offloading modeling for edge computing

Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

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A comprehensive review on privacy preserving data mining

The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in communication, computing, caching and control (4Cs) problems. The recent advances in deep reinforcement learning (DRL) algorithms can potentially address the above problems of IoT systems. In this context, this paper provides a comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications. In particular, we first briefly review the state-of-the-art DRL algorithms and present a comprehensive analysis on their advantages and challenges. We then discuss on applying DRL algorithms to a wide variety of IoT applications including smart grid, intelligent transportation systems, industrial IoT applications, mobile crowdsensing, and blockchain-empowered IoT. Meanwhile, the discussion of each IoT application domain is accompanied by an in-depth summary and comparison of DRL algorithms. Moreover, we highlight emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.

Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing

Cloud computing has revolutionized the way computing and software services are delivered to the clients on demand. It offers users the ability to connect to computing resources and access IT managed services with a previously unknown level of ease. Due to this greater level of flexibility, the cloud has become the breeding ground of a new generation of products and services. However, the flexibility of cloud-based services comes with the risk of the security and privacy of users' data. Thus, security concerns among users of the cloud have become a major barrier to the widespread growth of cloud computing. One of the security concerns of cloud is data mining based privacy attacks that involve analyzing data over a long period to extract valuable information. In particular, in current cloud architecture a client entrusts a single cloud provider with his data. It gives the provider and outside attackers having unauthorized access to cloud, an opportunity of analyzing client data over a long period to extract sensitive information that causes privacy violation of clients. This is a big concern for many clients of cloud. In this paper, we first identify the data mining based privacy risks on cloud data and propose a distributed architecture to eliminate the risks.

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An Approach to Protect the Privacy of Cloud Data from Data Mining Based Attacks

The proliferation in the use of the Internet of Things (IoT) and Machine Learning (ML) techniques in edge computing systems have paved the way of using Intelligent Cognitive Assistants (ICA) for assisting people in working, learning, transportation, healthcare, and other activities. A challenge here is how to schedule application tasks between the three tiers in the edge computing system (i.e., remote cloud, fog and edge devices) according to several considered factors such as latency, energy, and bandwidth consumption. However, the state-of-the-art approaches for this challenge fall short in providing a schedule in real time for critical ICA tasks due to complex calculation phase. In this paper, we propose a novel ReInforcement Learning based Task Assignment approach, RILTA, that ensures the timeliness guaranteed execution of ICA tasks with high energy efficiency. We first formulate the task-scheduling problem in the edge computing systems considering timeliness and energy consumption in ICA applications. We then propose a heuristic for solving the problem and design the reinforcement model based on the output of the proposed heuristic. Our simulation results show that RILTA can reduce the task processing time and energy consumption with higher timeliness guarantee in comparison to other existing methods by 13 − 22% and 1 − 10% respectively.

Machine Learning based Timeliness-Guaranteed and Energy-Efficient Task Assignment in Edge Computing Systems

The key to optimizing the performance of an anycast-based system (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naive approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naive approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.

AnyOpt: predicting and optimizing IP Anycast performance

Connected and autonomous vehicles (CAV) have been introduced to increase roadway safety and traffic flow efficiency. In CAV scenarios, an autonomous vehicle shares its current and near-future driving maneuvers in terms of different driving signals (e.g., speed, brake pedal pressure) with its nearby vehicles using wireless communication technologies. Deep neural network (DNN) models are usually used to process the driving maneuver time-series data over other machine learning algorithms due to the high prediction accuracy of DNN models. In this scenario, an attacker can send false driving maneuver signals to fool the DNN model to misclassify an input. The existing black-box adversarial attacks (which are for image datasets) require many queries to the DNN model to check if a generated attack will be successful (hence long time) or high amount of perturbation (low imperceptibility), and thus cannot be applied to the time-sensitive CAV scenarios featured by multi-dimensional time series driving data. In this paper, we present an Advanced black-box Adversarial Attack $({\mathrm {A}}^{3})$ for the deep driving maneuver classification models. We first formulate an optimization problem for the attack generation with continuous search space to reduce the search time. To solve the optimization problem, A3 innovatively combines the binary search and optimization algorithm to improve the time-efficiency of searching the optimal solution. It first uses a binary partition technique to reduce the perturbation search space in solving the problem to improve time-efficiency. It then uses the zeroth-order stochastic gradient descent approach, which is featured by searching a solution faster for high-dimensional datasets, thus further improving time-efficiency. We evaluate the proposed A3 attack in terms of different metrics using two real driving datasets. The experimental results show that the A3 attack requires up to 84.12% fewer queries and 57.67% less perturbation with 94.87% higher success rates than the existing black-box adversarial attacks.

An Advanced Black-Box Adversarial Attack for Deep Driving Maneuver Classification Models

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

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Tanmoy Sen

Papers

An Approach to Protect the Privacy of Cloud Data from Data Mining Based Attacks

Machine Learning based Timeliness-Guaranteed and Energy-Efficient Task Assignment in Edge Computing Systems

AnyOpt: predicting and optimizing IP Anycast performance

An Advanced Black-Box Adversarial Attack for Deep Driving Maneuver Classification Models

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine