Search

Your search keyword '"Asheralieva, Alia"' showing total 109 results
Start Over Author "Asheralieva, Alia"
109 results on '"Asheralieva, Alia"'

1. BriDe Arbitrager: Enhancing Arbitrage in Ethereum 2.0 via Bribery-enabled Delayed Block Production

Author

Yang, Hulin, Li, Mingzhe, Zhang, Jin, Asheralieva, Alia, Wei, Qingsong, and Goh, Siow Mong Rick

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Cryptography and Security
Abstract

The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leverages Bribery-driven attacks to Delay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited (< 1/4) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.66 ETH (16,442.23 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.

Published
2024

2. A Survey of Coded Distributed Computing

Author

Ng, Jer Shyuan, Lim, Wei Yang Bryan, Luong, Nguyen Cong, Xiong, Zehui, Asheralieva, Alia, Niyato, Dusit, Leung, Cyril, and Miao, Chunyan

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Distributed computing has become a common approach for large-scale computation of tasks due to benefits such as high reliability, scalability, computation speed, and costeffectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC

Published
2020

11. Secure and Efficient Coded Multi-Access Edge Computing With Generalized Graph Neural Networks

Author

Asheralieva, Alia and Niyato, Dusit

Abstract

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs’ types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) – a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

Published
2023
Full Text
View/download PDF

13. Delay and Loss Due to Uplink Packet Scheduling in LTE Network

Author

Asheralieva, Alia, Mahata, Kaushik, Khan, Jamil Y., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bianchi, Giuseppe, editor, Lyakhov, Andrey, editor, and Khorov, Evgeny, editor

Published
2013
Full Text
View/download PDF

45. A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets.

Author

Kanwal, Tehsin, Anjum, Adeel, Khan, Abid, Asheralieva, Alia, and Jeon, Gwanggil

Subjects
ELECTRONIC health records, PETRI nets, DATA privacy, DATA transmission systems, ACQUISITION of data, DATA recorders & recording, INTERNET privacy
Abstract

The collection of private health data without compromising privacy is an imperative aspect of privacy‐aware data collection mechanisms. Privacy‐preserved data collection is achieved by anonymizing private data before its transmission from data holders to data collectors. Though there exist ample literature on private data collection for 1:1 (single record of a data holder) datasets, collecting multi‐records (multiple records of a data holder) datasets (referred to as 1:M datasets) has not received due attention from the research community. Therefore, the current studies experience serious privacy breaches in 1:M dataset thereby limiting their application in secure healthcare applications and systems. In this work, we have formally classified main privacy disclosures on these data collection mechanisms and proposed an improved privacy scheme, namely, horizontal sliced permuted permutation (H‐SPP) for 1:M datasets. It uses the composite slicing and anatomy‐based approach to protect against the privacy violations like identity, attribute, and membership disclosures. Moreover, we perform formal modeling of the proposed scheme using high‐level Petri nets (HLPN) and show that it effectively prevents the identified external and internal privacy attacks. Experimental results show that H‐SPP provides robust privacy for health data with high performance. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results