bluuebunny/crossref_metadata_2025_split huggingface dataset:
New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production
Authors: D’Agaro Edo , Rosa Franco , Akentieva Natalia P. 
Published: July 2021

Abstract
Agriculture 4.0, a combination of mechanical innovation and information and communication technologies (ICT) using precision farming, omics technologies and advanced waste treatment techniques, can be used to enhance the biological potential of animal and crop productions and reduce livestock gaseous emissions. In addition to animal proteins being excellent nutritional ingredients for the human diet, there is a growing concern regarding the amount of energy spent converting vegetable crops into animal protein and the relevant environmental impacts. Using the value chain analysis derived from the neoclassic production theory extended to industrial processing and the market, the hypothesis to be tested concerns the sustainability and convenience of different protein sources. The methodology implies the use of life cycle analysis (LCA) to evaluate the efficiency of different livestock diet ingredients. The use of feeding products depend upon various factors, including cost reduction, consumer acceptance, incumbent industry response, civil society support, policy consensus, lower depletion of natural resources, improved sustainable agri-food supply chain and LCA. EU policy makers should be aware of these changes in livestock and market chains and act proactively to encourage the use of alternative animal proteins.
Effect of storage time on the biodegradability of olive oil mill wastewater from the cold extraction of olive oil system
Authors: Gueboudji Zakia , Bagues Mohamed , Kadi Kenza , Nagaz Kamel , Addad Dalila 
Published: July 2021

Abstract
The olive oil processing industry’s liquid effluents (OMW) have a polluting capacity for the ecosystems. This study aimed to evaluate the physicochemical quality of OMW of two varieties of separated and combined olives cultivated in Khenchela Eastern Algeria, from the cold extraction of the olive oil extraction system. These was to determine their degree of pollution and biodegradability during one year of storage at ambient temperature to recommend the correct treatment for each storage time. Results of the measured parameters pH, EC,TSS percent, H2O percent, lipids, DM, OM, MM, VM, COT percent, NTK percent, C/N, BOD5, COD, BI, TOM, BOD5/COD show that wastewater from olive oil mills has an acid pH, and they are very loaded with organic matter evaluated in terms of COD and BOD5, quite filled with minerals. The storage of olive mill waste can reduce progressively the pollution caused by this waste. Whereas during one year, the reduction rate of COD, BOD5, TOM, BI, is respectively 29.4%, 54.8%, 39.16%, 54.2%, but C/N, BOD5/COD continue to increase as well as pH that continues to decrease during the storage. Accordingly, storing olive mill waste during a year reduces its pollution rate, so it is slowly biodegradable. When disposing of it, an adequate treatment procedure must be required to protect the environment.
Investigation on Control Strategies for a Single-Phase Photovoltaic Inverter Using PSCAD/EMTDC Software
Authors: Henz Cristiano Luiz , Gasparin Fabiano Perin 
Published: July 2021

Abstract
In the last decades, electric power produced through photovoltaic conversion has been increasing because of the need to reduce fossil fuel burning. Recently, photovoltaic systems have become more competitive and their role in the renewable energies market share is steadily gaining in importance. Improvements in the power electronics employed in the DC/AC conversion are topics of interest in the quest for more efficient and eventually reduced-cost inverters. The goal of this paper is to perform an investigation of control strategies and propose a topology for a single-phase DC/AC converter for photovoltaic arrays using the simulation software Power System Computer Aided Design/ Electromagnetic Transient Design and Control (PSCAD/EMTDC). The circuit proposed in this paper employs an isolating transformer to a grid-connected photovoltaic inverter. The control strategy proposed uses the instantaneous reactive power theory (p–q theory) and phase-locked loop (PLL). The p-q theory uses two virtual axes in the Park Transformation, which provide to the control system a good dynamic response, accuracy, and decoupling between the control and power system. Computer simulations using the electromagnetic transient software PSCAD show the efficiency of the proposed strategy for a single-phase inverter. The control strategy and topology are quite simple and easy to implement in the future using a Digital Signal Processor (DSP). The results provide insights into new power electronics solutions, which can improve the efficiency and efficacy of the current available in DC/AC converters for photovoltaic systems.
Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive
Authors: Jankowska Kamila , Ewert Pawel 
Published: July 2021

Abstract
Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.
ZKSENSE: A Friction-less Privacy-Preserving Human Attestation Mechanism for Mobile Devices
Authors: Querejeta-Azurmendi Iñigo , Papadopoulos Panagiotis , Varvello Matteo , Nappa Antonio , Zhang Jiexin , Livshits Benjamin 
Published: July 2021

Abstract
Recent studies show that 20.4% of the internet traffic originates from automated agents. To identify and block such ill-intentioned traffic, mechanisms that verify the humanness of the user are widely deployed, with CAPTCHAs being the most popular. Traditional CAPTCHAs require extra user effort (e.g., solving mathematical puzzles), which can severely downgrade the end-user’s experience, especially on mobile, and provide sporadic humanness verification of questionable accuracy. More recent solutions like Google’s reCAPTCHA v3, leverage user data, thus raising significant privacy concerns. To address these issues, we present zkSENSE: the first zero-knowledge proof-based humanness attestation system for mobile devices. zkSENSE moves the human attestation to the edge: onto the user’s very own device, where humanness of the user is assessed in a privacy-preserving and seamless manner. zkSENSE achieves this by classifying motion sensor outputs of the mobile device, based on a model trained by using both publicly available sensor data and data collected from a small group of volunteers. To ensure the integrity of the process, the classification result is enclosed in a zero-knowledge proof of humanness that can be safely shared with a remote server. We implement zkSENSE as an Android service to demonstrate its effectiveness and practicality. In our evaluation, we show that zkSENSE successfully verifies the humanness of a user across a variety of attacking scenarios and demonstrate 92% accuracy. On a two years old Samsung S9, zkSENSE’s attestation takes around 3 seconds (when visual CAPTCHAs need 9.8 seconds) and consumes a negligible amount of battery.
“I would have to evaluate their objections”: Privacy tensions between smart home device owners and incidental users
Authors: Cobb Camille , Bhagavatula Sruti , Garrett Kalil Anderson , Hoffman Alison , Rao Varun , Bauer Lujo 
Published: July 2021

Abstract
Recent research and articles in popular press have raised concerns about the privacy risks that smart home devices can create for incidental users—people who encounter smart home devices that are owned, controlled, and configured by someone else. In this work, we present the results of a user-centered investigation that explores incidental users’ experiences and the tensions that arise between device owners and incidental users. We conducted five focus group sessions through which we identified specific contexts in which someone might encounter other people’s smart home devices and the main concerns device owners and incidental users have in such situations. We used these findings to inform the design of a survey instrument, which we deployed to a demographically representative sample of 386 adults in the United States. Through this survey, we can better understand which contexts and concerns are most bothersome and how often device owners are willing to accommodate incidental users’ privacy preferences. We found some surprising trends in terms of what people are most worried about and what actions they are willing to take. For example, while participants who did not own devices themselves were often uncomfortable imagining them in their own homes, they were not as concerned about being affected by such devices in homes that they entered as part of their jobs. Participants showed interest in privacy solutions that might have a technical implementation component, but also frequently envisioned an open dialogue between incidental users and device owners to negotiate privacy accommodations.
Less is More: A privacy-respecting Android malware classifier using federated learning
Authors: Gálvez Rafa , Moonsamy Veelasha , Diaz Claudia 
Published: July 2021

Abstract
In this paper we present LiM (‘Less is More’), a malware classification framework that leverages Federated Learning to detect and classify malicious apps in a privacy-respecting manner. Information about newly installed apps is kept locally on users’ devices, so that the provider cannot infer which apps were installed by users. At the same time, input from all users is taken into account in the federated learning process and they all benefit from better classification performance. A key challenge of this setting is that users do not have access to the ground truth (i.e. they cannot correctly identify whether an app is malicious). To tackle this, LiM uses a safe semi-supervised ensemble that maximizes classification accuracy with respect to a baseline classifier trained by the service provider (i.e. the cloud). We implement LiM and show that the cloud server has F1 score of 95%, while clients have perfect recall with only 1 false positive in > 100 apps, using a dataset of 25K clean apps and 25K malicious apps, 200 users and 50 rounds of federation. Furthermore, we conduct a security analysis and demonstrate that LiM is robust against both poisoning attacks by adversaries who control half of the clients, and inference attacks performed by an honest-but-curious cloud server. Further experiments with Ma-MaDroid’s dataset confirm resistance against poisoning attacks and a performance improvement due to the federation.
SoK: Privacy-Preserving Computation Techniques for Deep Learning
Authors: Cabrero-Holgueras José , Pastrana Sergio 
Published: July 2021

AbstractDeep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.
LogPicker: Strengthening Certificate Transparency Against Covert Adversaries
Authors: Dirksen Alexandra , Klein David , Michael Robert , Stehr Tilman , Rieck Konrad , Johns Martin 
Published: July 2021

Abstract
HTTPS is a cornerstone of privacy in the modern Web. The public key infrastructure underlying HTTPS, however, is a frequent target of attacks. In several cases, forged certificates have been issued by compromised Certificate Authorities (CA) and used to spy on users at large scale. While the concept of Certificate Transparency (CT) provides a means for detecting such forgeries, it builds on a distributed system of CT logs whose correctness is still insufficiently protected. By compromising a certificate authority and the corresponding log, a covert adversary can still issue rogue certificates unnoticed.
We introduce LogPicker, a novel protocol for strengthening the public key infrastructure of HTTPS. LogPicker enables a pool of CT logs to collaborate, where a randomly selected log includes the certificate while the rest witness and testify the certificate issuance process. As a result, CT logs become capable of auditing the log in charge independently without the need for a trusted third party. This auditing forces an attacker to control each participating witness, which significantly raises the bar for issuing rogue certificates. LogPicker is efficient and designed to be deployed incrementally, allowing a smooth transition towards a more secure Web.
“We, three brothers have always known everything of each other”: A Cross-cultural Study of Sharing Digital Devices and Online Accounts
Authors: Al-Ameen Mahdi Nasrullah , Kocabas Huzeyfe , Nandy Swapnil , Tamanna Tanjina 
Published: July 2021

Abstract
Although many technologies assume that a device or an account would be used by a single user, prior research has found that this assumption may not hold true in everyday life. Most studies conducted to date focused on sharing a device or account with the members in a household. However, there is a dearth in existing literature to understand the contexts of sharing devices and accounts, which may extend to a wide range of personal, social, and professional settings. Further, people’s sharing behavior could be impacted by their social background. To this end, our paper presents a qualitative study with 59 participants from three different countries: Bangladesh, Turkey, and USA, where we investigated the sharing of digital devices (e.g., computer, mobile phone) and online accounts, in particular, financial and identity accounts (e.g., email, social networking) in various contexts, and with different entities - not limited to the members in a household. Our study reveals users’ perceptions of risks while sharing a device or account, and their access control strategies to protect privacy and security. Based on our analysis, we shed light on the interplay between users’ sharing behavior and their demographics, social background, and cultural values. Taken together, our findings have broad implications that advance the PETS community’s situated understanding of sharing devices and accounts.
SoK: Efficient Privacy-preserving Clustering
Authors: Hegde Aditya , Möllering Helen , Schneider Thomas , Yalame Hossein 
Published: July 2021

AbstractClustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today’s four most efficient fully private clustering protocols by Cheon et al. (SAC’19), Meng et al. (ArXiv’19), Mohassel et al. (PETS’20), and Bozdemir et al. (ASIACCS’21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.
Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
Authors: Mouchet Christian , Troncoso-Pastoriza Juan , Bossuat Jean-Philippe , Hubaux Jean-Pierre 
Published: July 2021

Abstract
We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce a multiparty version of the Brakerski-Fan-Vercauteren homomorphic cryptosystem and implement it in an open-source library. MHE-based MPC solutions have several advantages: Their transcript is public, their o~ine phase is compact, and their circuit-evaluation procedure is noninteractive. By exploiting these properties, the communication complexity of MPC tasks is reduced from quadratic to linear in the number of parties, thus enabling secure computation among potentially thousands of parties and in a broad variety of computing paradigms, from the traditional peer-to-peer setting to cloud-outsourcing and smart-contract technologies. MHE-based approaches can also outperform the state-of-the-art solutions, even for a small number of parties. We demonstrate this for three circuits: private input selection with application to private-information retrieval, component-wise vector multiplication with application to private-set intersection, and Beaver multiplication triples generation. For the first circuit, privately selecting one input among eight thousand parties’ (of 32 KB each) requires only 1.31 MB of communication per party and completes in 61.7 seconds. For the second circuit with eight parties, our approach is 8.6 times faster and requires 39.3 times less communication than the current methods. For the third circuit and ten parties, our approach generates 20 times more triples per second while requiring 136 times less communication per-triple than an approach based on oblivious transfer. We implemented our scheme in the Lattigo library and open-sourced the code at github.com/ldsec/lattigo.
CrowdNotifier: Decentralized Privacy-Preserving Presence Tracing
Authors: Lueks Wouter , Gürses Seda , Veale Michael , Bugnion Edouard , Salathé Marcel , Paterson Kenneth G. , Troncoso Carmela 
Published: July 2021

Abstract
There is growing evidence that SARS-CoV-2 can be transmitted beyond close proximity contacts, in particular in closed and crowded environments with insufficient ventilation. To help mitigation efforts, contact tracers need a way to notify those who were present in such environments at the same time as infected individuals. Neither traditional human-based contact tracing powered by handwritten or electronic lists, nor Bluetooth-enabled proximity tracing can handle this problem efficiently. In this paper, we propose CrowdNotifier, a protocol that can complement manual contact tracing by efficiently notifying visitors of venues and events with SARS-CoV-2-positive attendees. We prove that CrowdNotifier provides strong privacy and abuse-resistance, and show that it can scale to handle notification at a national scale.
Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting
Authors: Hoang Nguyen Phong , Niaki Arian Akhavan , Gill Phillipa , Polychronakis Michalis 
Published: July 2021

AbstractAlthough the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by—the still exposed—IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain–IP mappings unstable, and prevent straightforward IP-based browsing tracking.In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website finger-printing technique that allows a network-level observer to identifyat scalethe website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popularandsensitive web-sites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.
Mercurial Signatures for Variable-Length Messages
Authors: Crites Elizabeth C. , Lysyanskaya Anna 
Published: July 2021

Abstract
Mercurial signatures are a useful building block for privacy-preserving schemes, such as anonymous credentials, delegatable anonymous credentials, and related applications. They allow a signature σ on a message m under a public key pk to be transformed into a signature σ′ on an equivalent message m′ under an equivalent public key pk′ for an appropriate notion of equivalence. For example, pk and pk′ may be unlinkable pseudonyms of the same user, and m and m′ may be unlinkable pseudonyms of a user to whom some capability is delegated. The only previously known construction of mercurial signatures suffers a severe limitation: in order to sign messages of length ℓ, the signer’s public key must also be of length ℓ. In this paper, we eliminate this restriction and provide an interactive signing protocol that admits messages of any length. We prove our scheme existentially unforgeable under chosen open message attacks (EUF-CoMA) under a variant of the asymmetric bilinear decisional Diffie-Hellman assumption (ABDDH).