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Companies that try to address inequality in employment face a hiring paradox. Failing to address workforce imbalance can result in legal sanctions and scrutiny, but proactive measures to address these issues might result in the same legal conflict. Recent run-ins of Microsoft and Wells Fargo with the Labor Department's Office of Federal Contract Compliance Programs (OFCCP) are not isolated and are likely to persist. To add to the confusion, existing scholarship on Ricci v. DeStefano often deems solutions to this paradox impossible. Circumventive practices such as the 4/5ths rule further illustrate tensions between too little action and too much action. In this work, we give a powerful way to solve this hiring paradox that tracks both legal and algorithmic challenges. We unpack the nuances of Ricci v. DeStefano and extend the legal literature arguing that certain algorithmic approaches to employment are allowed by introducing the legal practice of banding to evaluate candidates. We thus show that a bias-aware technique can be used to diagnose and mitigate "built-in" headwinds in the employment pipeline. We use the machinery of partially ordered sets to handle the presence of uncertainty in evaluations data. This approach allows us to move away from treating "people as numbers" to treating people as individuals -- a property that is sought after by Title VII in the context of employment.

A Bio-metrics system is actually a pattern recognition system that utilizes various patterns like iris, retina and biological traits like fingerprint, voice recognition, facial geometry and hand geometry. What makes Bio-metrics really attractive is that the various security codes like passwords and ID cards can be interchanged, stolen or duplicated. To enhance the security and reliability of the system, physiological traits can be used. This paper gives the overview of key bio-metric technologies and basic techniques involved and their drawbacks. Then the paper illustrates the working of ECG and the various opportunities for ECG are also mentioned.

Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide, that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.

Supply chain security has become a growing concern in security risk analysis of the Internet of Things (IoT) systems. Their highly connected structures have significantly enlarged the attack surface, making it difficult to track the source of the risk posed by malicious or compromised suppliers. This chapter presents a system-scientific framework to study the accountability in IoT supply chains and provides a holistic risk analysis technologically and socio-economically. We develop stylized models and quantitative approaches to evaluate the accountability of the suppliers. Two case studies are used to illustrate accountability measures for scenarios with single and multiple agents. Finally, we present the contract design and cyber insurance as economic solutions to mitigate supply chain risks. They are incentive-compatible mechanisms that encourage truth-telling of the supplier and facilitate reliable accountability investigation for the buyer.

Recently, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized applications (e.g., Internet of Things (IoT)). However, its integration with the IoT is still facing several problems (e.g., scalability, flexibility). Provisioning resources to enable a large number of connected IoT devices implies having a scalable and flexible blockchain. To address these issues, we propose a scalable and trustworthy blockchain (STB) architecture that is suitable for the IoT; which uses blockchain sharding and oracles to establish trust among unreliable IoT devices in a fully distributed and trustworthy manner. In particular, we design a Peer-To-Peer oracle network that ensures data reliability, scalability, flexibility, and trustworthiness. Furthermore, we introduce a new lightweight consensus algorithm that scales the blockchain dramatically while ensuring the interoperability among participants of the blockchain. The results show that our proposed STB architecture achieves flexibility, efficiency, and scalability making it a promising solution that is suitable for the IoT context.

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.

Latent Dirichlet Allocation(LDA) is a popular topic model. Given the fact that the input corpus of LDA algorithms consists of millions to billions of tokens, the LDA training process is very time-consuming, which may prevent the usage of LDA in many scenarios, e.g., online service. GPUs have benefited modern machine learning algorithms and big data analysis as they can provide high memory bandwidth and computation power. Therefore, many frameworks, e.g. Ten- sorFlow, Caffe, CNTK, support to use GPUs for accelerating the popular machine learning data-intensive algorithms. However, we observe that LDA solutions on GPUs are not satisfying. In this paper, we present CuLDA_CGS, a GPU-based efficient and scalable approach to accelerate large-scale LDA problems. CuLDA_CGS is designed to efficiently solve LDA problems at high throughput. To it, we first delicately design workload partition and synchronization mechanism to exploit the benefits of mul- tiple GPUs. Then, we offload the LDA sampling process to each individual GPU by optimizing from the sampling algorithm, par- allelization, and data compression perspectives. Evaluations show that compared with state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU. CuLDA_CGS is able to achieve extra 3.0X speedup on 4 GPUs. The source code is publicly available on //github.com/cuMF/ CuLDA_CGS.