Performance Analysis of Blockchain Platforms

Blockchain technologies have drawn massive attention to the world these past few years mostly because of the burst of cryptocurrencies like Bitcoin, Etherium, Ripple and many others. A Blockchain, also known as distributed ledger technology, has demonstrated huge potential in saving time and costs. This open-source technology which generates a decentralized public ledger of transactions is widely appreciated for ensuring a high level of privacy through encryption and thus sharing the transaction details only amongst the participants involved in the transactions. The Blockchain is used not only for cryptocurrency but also by various companies to meet their business ends, such as efficient management of supply chains and logistics. The rise and fall of numerous crypto-currencies based on blockchain technology have generated debate among tech-giants and regulatory bodies. There are various groups which are working on standardizing the blockchain technology. At the same time, numerous groups are actively working, developing and fine-tuning their own blockchain platforms. Platforms such as etherium, hyperledger, parity, etc. have their own pros and cons. This research is focused on the performance analysis of blockchain platforms which gives a comparative understanding of these platforms

Learning structure and schemas from heterogeneous domains in networked systems: a survey

The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.Peer ReviewedPostprint (published version

PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

Blockchain-based access control management for Decentralized Online Social Networks

Online Social Networks (OSNs) represent today a big communication channel where users spend a lot of time to share personal data. Unfortunately, the big popularity of OSNs can be compared with their big privacy issues. Indeed, several recent scandals have demonstrated their vulnerability. Decentralized Online Social Networks (DOSNs) have been proposed as an alternative solution to the current centralized OSNs. DOSNs do not have a service provider that acts as central authority and users have more control over their information. Several DOSNs have been proposed during the last years. However, the decentralization of the social services requires efficient distributed solutions for protecting the privacy of users. During the last years the blockchain technology has been applied to Social Networks in order to overcome the privacy issues and to offer a real solution to the privacy issues in a decentralized system. However, in these platforms the blockchain is usually used as a storage, and content is public. In this paper, we propose a manageable and auditable access control framework for DOSNs using blockchain technology for the definition of privacy policies. The resource owner uses the public key of the subject to define auditable access control policies using Access Control List (ACL), while the private key associated with the subject's Ethereum account is used to decrypt the private data once access permission is validated on the blockchain. We provide an evaluation of our approach by exploiting the Rinkeby Ethereum testnet to deploy the smart contracts. Experimental results clearly show that our proposed ACL-based access control outperforms the Attribute-based access control (ABAC) in terms of gas cost. Indeed, a simple ABAC evaluation function requires 280,000 gas, instead our scheme requires 61,648 gas to evaluate ACL rules

Advances in the Convergence of Blockchain and Artificial Intelligence

Blockchain (BC) and artificial intelligence (AI) are currently two of the hottest computer science topics and their future seems bright. However, their convergence is not straightforward, and more research is needed in both fields. Thus, this book presents some of the latest advances in the convergence of BC and AI, gives useful guidelines for future researchers on how BC can help AI and how AI can become smarter, thanks to the use of BC. This book specifically analyzes the past of BC through the history of Bitcoin and then looks into the future: from massive internet-of-things (IoT) deployments, to the so-called metaverse, and to the next generation of AI-powered BC-based cyber secured applications

Communication-Efficient Cluster Federated Learning in Large-scale Peer-to-Peer Networks

A traditional federated learning (FL) allows clients to collaboratively train a global model under the coordination of a central server, which sparks great interests in exploiting the private data distributed on clients. However, once the central server suffers from a single point of failure, it will lead to system crash. In addition, FL usually involves a large number of clients, which requires expensive communication costs. These challenges inspire a communication-efficient design of decentralized FL. In this paper, we propose an efficient and privacy-preserving global model training protocol in the context of FL in large-scale peer-to-peer networks, CFL. The proposed CFL protocol aggregates local contributions hierarchically by a cluster-based aggregation mode, as well as a leverged authenticated encryption scheme to ensure the security communication, whose key is distributed by a modified secure communication key establishment protocol. Theoretical analyses show that CFL guarantees the privacy of local model update parameters, as well as integrity and authenticity under the widespread internal semi-honest and external malicious threat models. In particular, the proposed key revocation based on public voting can effectively defense against external adversaries hijacking honest participants to ensure the confidentiality of the communication keys. Moreover, the modified secure communication key establishment protocol indeed achieves high network connectivity probability to ensure transmission security of the system

Innovate Magazine / Annual Review 2011-2012

This year\u27s issue highlights some of the ways the SJSU School of Library and Information Science is being a catalyst for global innovation, explores the tools SJSU SLIS master\u27s students and faculty use to interact in our innovative online learning environment, and describes some of the exciting career pathways our alum are pursuing.https://scholarworks.sjsu.edu/innovate/1000/thumbnail.jp

When energy trading meets blockchain in electrical power system: The state of the art

With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.This research was funded by Beijing Natural Science Foundation (grant number 4182060).Scopu