Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

In the growing world of artificial intelligence, federated learning is a distributed learning framework enhanced to preserve the privacy of individuals' data. Federated learning lays the groundwork for collaborative research in areas where the data is sensitive. Federated learning has several implications for real-world problems. In times of crisis, when real-time decision-making is critical, federated learning allows multiple entities to work collectively without sharing sensitive data. This distributed approach enables us to leverage information from multiple sources and gain more diverse insights. This paper is a systematic review of the literature on privacy-preserving machine learning in the last few years based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have presented an extensive review of supervised/unsupervised machine learning algorithms, ensemble methods, meta-heuristic approaches, blockchain technology, and reinforcement learning used in the framework of federated learning, in addition to an overview of federated learning applications. This paper reviews the literature on the components of federated learning and its applications in the last few years. The main purpose of this work is to provide researchers and practitioners with a comprehensive overview of federated learning from the machine learning point of view. A discussion of some open problems and future research directions in federated learning is also provided

Improving Data Security in Public Cloud Storage with the Implementation of Data Obfuscation and Steganography Techniques

Cloud computing is a widely used distribution paradigm for delivering secure information services over the internet. The advantages of cloud computing include the capacity to remotely access one's data from any location, eliminating concerns over data backups, as well as the establishment of disaster recovery and business continuity facilities. Nevertheless, cloud computing gives rise to apprehensions over the appropriate management of information and interactions by cloud service providers, user organisations, and governments. Cloud computing has become an increasingly popular choice for both big organisations and individuals seeking cost-effective access to a wide range of network services. Typically, individuals' information is kept on a public Cloud, which is accessible to everybody. This basic gives rise to several concerns that are contrary to the adaptable services offered by cloud providers, such as Confidentiality, Integrity, Availability, Authorization, and others. Currently, there are several choices available for safeguarding data, with encryption being the most favoured one. Encryption alone is insufficient for adequately safeguarding the sensitive information of many users. Additionally, the encryption and decryption procedure for each every query requires a greater amount of time. Furthermore, it is not advisable to just prioritise user-centric thinking, since users relinquish direct control over their data once it is uploaded to Cloud premises. Given this reality, it is important to contemplate the security of users' vital information on the Cloud server. This may be achieved by the use of the crucial method known as obfuscation. In order to alleviate the load on the Cloud server and provide sufficient security for user data, we suggest an approach that combines both strategies, namely... The thesis explores the concepts of obfuscation and encryption. If the files or documents need security, the user data may be encrypted. The Cloud's DaaS service is protected utilising obfuscation methods. By using a dual-pronged strategy, the suggested technique provides enough protection for anonymous access and ensures the preservation of privacy, even while dealing with information stored on Cloud servers. The objective is to provide a robust integrity checking method, an enhanced access control mechanism, and a group sharing mechanism. These improvements seek to reduce the workload and foster a higher degree of confidence between clients and service providers

Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data

Should Acoustic Simulation Technology be Utilised in Architectural Practice? Does it have the Potential for BIM Integration?

The research presented in this paper, firstly, aims to convey the importance of our acoustic environment through focusing on the effects of undesirable acoustic conditions on cognitive abilities in spaces where cognitive performance is of the utmost concern, our learning environments. Secondly, it aims to investigate current state-of-the-art acoustic simulation methods, available platforms, and their levels of interoperability with architectural BIM authoring software. Structured interviews were carried out with 7 Irish architects and architectural technologists to determine if a disconnection between architectural design and acoustic performance exists and to identify the advantages and disadvantages of current workflows for acoustic performance evaluation. Additionally, industry opinions were gathered on whether it is measurable that our acoustic environments are at a disadvantage as a result of the apparent gap in available integrated acoustic evaluation solutions for a BIM-enabled design workflow, and finally to investigate industry demand for better integration of acoustic evaluation tools with BIM authoring platforms

Privacy-Preserving Cloud-Assisted Data Analytics

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users\u27 data may contain private information that needs to be protected. Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics. The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems. To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users. To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user\u27s data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee. The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee. In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures

Peekaboo: A Hub-Based Approach to Enable Transparency in Data Processing within Smart Homes (Extended Technical Report)

We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data pre-processing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.Comment: 18 page

The (higher-order) evidential significance of attention and trust—comments on Levy’s Bad Beliefs

The work was supported by the H2020 European Research Council [ERC-2017-CoG 771074].In Bad Beliefs, Levy presents a picture of belief-forming processes according to which, on most matters of significance, we defer to reliable sources by relying extensively on cultural and social cues. Levy conceptualizes the kind of evidence provided by socio-cultural environments as higher-order evidence. But his notion of higher-order evidence seems to differ from those available in the epistemological literature on higher-order evidence, and this calls for a reflection on how exactly social and cultural cues are/count as/provide higher-order evidence. In this paper, I draw on the three-tiered model of epistemic exchange that I have been developing recently, which highlights the centrality of relations of attention and trust in belief-forming processes, to explicate how social and cultural cues provide higher-order evidence. I also argue that Levy’s account fails to sufficiently address the issue of strategic actors who have incentives to pollute epistemic environments for their benefit, and more generally the power struggles, incentives, and competing interests that characterize human sociality. Levy’s attempted reduction of the political to the epistemic ultimately fails, but his account of social and cultural cues as higher-order evidence offers an insightful perspective on epistemic social structures.Publisher PDFPeer reviewe

Aid for trade and African agriculture : the bittersweet case of Swazi sugar

In 2006, the European Union reformed its sugar regime, reducing the price for sugar by 36%. To cushion the impact on traditional overseas suppliers, an ‘Aid for Trade’ programme called the Accompanying Measures for Sugar Protocol countries (AMSP) was implemented. This paper explores the impacts of the AMSP in Swaziland. The authors discuss emergent agrarian class differentiation and argue that the benefits experienced by farmers are jeopardised by ongoing processes of liberalisation. The paper concludes by suggesting that donors must consider market stabilisation and corporate regulation if they are to make ‘Aid for Trade’ work for the poor

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation