bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of electrical impedance tomography and ultrasonography for determination of solid and cystic lesion resembling breast tumor embedded in chicken phantom
Authors: Choridah L. , Kurniadi D. , Ain K. , Ulum M.F. , Mukhaiyar U. , Garnadi A.D. , Setyawan N.H. 
Published: November 2021

Abstract
Ultrasonography (US) and Electrical Impedance Tomography (EIT) can be used to detect breast cancer. Ultrasonography is based on non-ionizing radiations without adverse biological effects. A set of electrodes was placed around the torso and a small alternating current (AC) was injected via two of the electrodes into the object. This study aimed to acquire preliminary data to evaluate the EIT method for differentiation of artificial solid and cystic tumors in comparison to standard US.
This study used a phantom made from chicken meat. In order to obtain the image of the solid tumor, an olive with carrot insertion was done, and the cystic tumor was created by filling a small balloon with water. GE Logic C5 ultrasound was performed with a 12 MHz linear transducer. For EIT measurement, 16 ECG electrodes and 32 ECG electrodes were placed. Data processing was done using the Graz consensus Reconstruction algorithm for EIT (GREIT) and Newton's One Step Error Reconstructor (NOSER) methods.
The artificial solid tumor produced an ultrasound image of an oval, inhomogeneous lesions. The GREIT method with 16 electrodes of artificial solid tumor did not show a match between the reconstructed image and the original object containing 2 anomalies, but a match was found with 32 electrodes. In the NOSER method, both 16 and 32 electrodes showed a match. Ultrasound of the artificial cystic tumor showed an oval, circumscribed, anechoic with posterior enhancement. Both the GREIT and NOSER methods using the artificial cystic tumor showed a match between the reconstructed image and the original object containing two anomalies.
EIT has a lower imaging resolution in comparison to ultrasonography, but is progressively maturing as a tool for monitoring and imaging. The solid and cystic anomalies on the phantoms were visualized by the GREIT and NOSER methods except for the solid anomaly with the GREIT 16 electrodes.
Reflections on the Use of AI in the Legal Domain
Authors: Colonna Liane 
Published: November 2021

AbstractThis paper examines the field of Artificial Intelligence (AI) and Law and offers some broad reflections on its current state. First, the paper introduces the concept of AI, paying particular attention to the distinction between hard and soft AI. Next, it considers how AI can be used to support (or replace!) legal work and legal reasoning. The paper goes on to explore applications of AI in the legal domain and concludes with some critical reflections on the use of AI in the legal context.
Understanding the effects of BIM implementation in corporation finance: An empirical study in China
Authors: Wang Zhimin , Ma Jianjun 
Published: November 2021

AbstractBuilding Information Modeling (BIM) has been acknowledged for promoting effectiveness and efficiency in project planning and management activities including design, bidding, and construction. Yet limited effort has been made to investigate the impacts of BIM implementation on corporate performance. Using the financial information of 314 Architecture, Engineering, and Construction (AEC) companies listed in the A-share market of China, this paper examines the economic benefits of BIM practice and assesses the influential mechanism of BIM implementation at the corporation level. The results reveal that BIM adoption contributes to better corporate profitability, especially in non-state-owned enterprises (non-SOEs). The positive impact of BIM implementation on profitability is achieved by the functions of promoting schedule efficiency and operation cost reduction. However, high initial BIM investment is still required in China's AEC companies at the current stage, and the effectiveness enhancement in corporation management has not been realized. By providing clear empirical evidence of the economic benefits of BIM adoption from a corporation perspective, this study might help improve top managers’ awareness, intention, or support of future BIM utilization in China's AEC companies.
Determination of construction process duration based on labor productivity estimation: A case study
Authors: Kubečková Darja , Smugala Stanislav 
Published: November 2021

Abstract
Monitoring labor productivity and how to decrease construction costs are the key issues in the planning process of a construction project. The CONTEC automated system combined with statistical methods assists in predicting the amount of time required to complete construction works according to the specified number of deployed work crews, technological processes, and labor required for certain production in person-hours. This study applies statistical analyses and probability theories for plastering work, which represents a labor-intensive construction process. The goal of the research is to determine the probability of completion of the construction process based on monitoring the mean value of performance. By application of statistical analyses a decrease in the performance standard has been proved compared with the planned values given in the CONTEC database. The decrease in performance, which was also caused by the number of days with unfavorable climatic conditions and demonstrated by performing interval estimates based on the collection of statistical data, was later confirmed by a relative frequency test. The measures taken were in terms of establishing the required number of personnel capacities for complying with the construction schedule.
Forward and Backward-Secure Range-Searchable Symmetric Encryption
Authors: Wang Jiafan , Chow Sherman S. M. 
Published: November 2021

Abstract
Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18).
We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off reducing client-side storage. Our schemes achieve forward security, an important property that mitigates file injection attacks. Moreover, we identify a variant of injection attacks against the first somewhat dynamic scheme (ESORICS’18). We also extend the definition of backward security to range DSSE and show that our schemes are compatible with a generic upgrade of backward security (CCS’17).
We comprehensively analyze the computation and communication overheads, including implementation details of client-side index-related operations omitted by prior schemes. We show high empirical efficiency for million-scale databases over a million-scale keyword space.
MLEFlow: Learning from History to Improve Load Balancing in Tor
Authors: Darir Hussein , Sibai Hussein , Cheng Chin-Yu , Borisov Nikita , Dullerud Geir , Mitra Sayan 
Published: November 2021

Abstract
Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities. The authorities generate these probability distributions based on estimates of the capacities of the relays. They compute these estimates based on the bandwidths of probes sent to the relays. These estimates are necessary for better load balancing. Unfortunately, current methods fall short of providing accurate estimates leaving the network underutilized and its capacities unfairly distributed between the users’ paths. We present MLEFlow, a maximum likelihood approach for estimating relay capacities for optimal load balancing in Tor. We show that MLEFlow generalizes a version of Tor capacity estimation, TorFlow-P, by making better use of measurement history. We prove that the mean of our estimate converges to a small interval around the actual capacities, while the variance converges to zero. We present two versions of MLEFlow: MLEFlow-CF, a closed-form approximation of the MLE and MLEFlow-Q, a discretization and iterative approximation of the MLE which can account for noisy observations. We demonstrate the practical benefits of MLEFlow by simulating it using a flow-based Python simulator of a full Tor network and packet-based Shadow simulation of a scaled down version. In our simulations MLEFlow provides significantly more accurate estimates, which result in improved user performance, with median download speeds increasing by 30%.
If This Context Then That Concern: Exploring users’ concerns with IFTTT applets
Authors: Saeidi Mahsa , Calvert McKenzie , Au Audrey W. , Sarma Anita , Bobba Rakesh B. 
Published: November 2021

Abstract
End users are increasingly using trigger-action platforms like If-This-Then-That (IFTTT) to create applets to connect smart-home devices and services. However, there are inherent implicit risks in using such applets—even non-malicious ones—as sensitive information may leak through their use in certain contexts (e.g., where the device is located, who can observe the resultant action). This work aims to understand to what extent end users can assess this implicit risk. More importantly we explore whether usage context makes a difference in end-users’ perception of such risks. Our work complements prior work that has identified the impact of usage context on expert evaluation of risks in IFTTT by focusing the impact of usage context on end-users’ risk perception. Through a Mechanical Turk survey of 386 participants on 49 smart-home IFTTT applets, we found that participants have a nuanced view of contextual factors and that different values for contextual factors impact end-users’ risk perception differently. Further, our findings show that nudging the participants to think about different usage contexts led them to think deeper about the associated risks and raise their concern scores.
Toward Uncensorable, Anonymous and Private Access Over Satoshi Blockchains
Authors: Recabarren Ruben , Carbunar Bogdan 
Published: November 2021

Abstract
Providing unrestricted access to sensitive content such as news and software is difficult in the presence of adaptive and resourceful surveillance and censoring adversaries. In this paper we leverage the distributed and resilient nature of commercial Satoshi blockchains to develop the first provably secure, censorship resistant, cost-efficient storage system with anonymous and private access, built on top of commercial cryptocurrency transactions. We introduce max-rate transactions, a practical construct to persist data of arbitrary size entirely in a Satoshi blockchain. We leverage max-rate transactions to develop UWeb, a blockchain-based storage system that charges publishers to self-sustain its decentralized infrastructure. UWeb organizes blockchain-stored content for easy retrieval, and enables clients to store and access content with provable anonymity, privacy and censorship resistance properties.
We present results from UWeb experiments with writing 268.21 MB of data into the live Litecoin blockchain, including 4.5 months of live-feed BBC articles, and 41 censorship resistant tools. The max-rate writing throughput (183 KB/s) and blockchain utilization (88%) exceed those of state-of-the-art solutions by 2-3 orders of magnitude and broke Litecoin’s record of the daily average block size. Our simulations with up to 3,000 concurrent UWeb writers confirm that UWeb does not impact the confirmation delays of financial transactions.
OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers
Authors: Cassel Darion , Lin Su-Chin , Buraggina Alessio , Wang William , Zhang Andrew , Bauer Lujo , Hsiao Hsu-Chun , Jia Limin , Libert Timothy 
Published: November 2021

Abstract
Over half of all visits to websites now take place in a mobile browser, yet the majority of web privacy studies take the vantage point of desktop browsers, use emulated mobile browsers, or focus on just a single mobile browser instead. In this paper, we present a comprehensive web-tracking measurement study on mobile browsers and privacy-focused mobile browsers. Our study leverages a new web measurement infrastructure, OmniCrawl, which we develop to drive browsers on desktop computers and smartphones located on two continents. We capture web tracking measurements using 42 different non-emulated browsers simultaneously. We find that the third-party advertising and tracking ecosystem of mobile browsers is more similar to that of desktop browsers than previous findings suggested. We study privacy-focused browsers and find their protections differ significantly and in general are less for lower-ranked sites. Our findings also show that common methodological choices made by web measurement studies, such as the use of emulated mobile browsers and Selenium, can lead to website behavior that deviates from what actual users experience.
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning
Authors: Usynin Dmitrii , Rueckert Daniel , Passerat-Palmbach Jonathan , Kaissis Georgios 
Published: November 2021

AbstractIn this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations asmodel adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.
AriaNN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
Authors: Ryffel Théo , Tholoniat Pierre , Pointcheval David , Bach Francis 
Published: November 2021

Abstract
We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data.
Our semi-honest 2-party computation protocol (with a trusted dealer) leverages function secret sharing, a recent lightweight cryptographic protocol that allows us to achieve an efficient online phase. We design optimized primitives for the building blocks of neural networks such as ReLU, MaxPool and BatchNorm. For instance, we perform private comparison for ReLU operations with a single message of the size of the input during the online phase, and with preprocessing keys close to 4× smaller than previous work. Last, we propose an extension to support n-party private federated learning.
We implement our framework as an extensible system on top of PyTorch that leverages CPU and GPU hardware acceleration for cryptographic and machine learning operations. We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet. We show that computation rather than communication is the main bottleneck and that using GPUs together with reduced key size is a promising solution to overcome this barrier.
Differentially private partition selection
Authors: Desfontaines Damien , Voss James , Gipson Bryant , Mandayam Chinmoy 
Published: November 2021

Abstract
Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we also need to make sure that the set of partitions released is also differentially private.
This problem is not new, and it was recently formally introduced as differentially private set union [14]. In this work, we continue this area of study, and focus on the common setting where each user is associated with a single partition. In this setting, we propose a simple, optimal differentially private mechanism that maximizes the number of released partitions. We discuss implementation considerations, as well as the possible extension of this approach to the setting where each user contributes to a fixed, small number of partitions.
Multiparty Reach and Frequency Histogram: Private, Secure, and Practical
Authors: Ghazi Badih , Kreuter Ben , Kumar Ravi , Manurangsi Pasin , Peng Jiayu , Skvortsov Evgeny , Wang Yao , Wright Craig 
Published: November 2021

AbstractConsider the setting where multiple parties each hold a multiset of users and the task is to estimate thereach(i.e., the number of distinct users appearing across all parties) and thefrequency histogram(i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.
Privacy-preserving FairSwap: Fairness and privacy interplay
Authors: Avizheh Sepideh , Haffey Preston , Safavi-Naini Reihaneh 
Published: November 2021

AbstractFair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.
Disparate Vulnerability to Membership Inference Attacks
Authors: Kulynych Bogdan , Yaghini Mohammad , Cherubin Giovanni , Veale Michael , Troncoso Carmela 
Published: November 2021

Abstract
A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model’s training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against different population subgroups. We first establish necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, using a notion of distributional generalization. Second, we derive connections of disparate vulnerability to algorithmic fairness and to differential privacy. We show that fairness can only prevent disparate vulnerability against limited classes of adversaries. Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model. We show that estimating disparate vulnerability by naïvely applying existing attacks can lead to overestimation. We then establish which attacks are suitable for estimating disparate vulnerability, and provide a statistical framework for doing so reliably. We conduct experiments on synthetic and real-world data finding significant evidence of disparate vulnerability in realistic settings.
Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
Authors: Jiang Xue , Zhou Xuebing , Grossklags Jens 
Published: November 2021

AbstractBusiness intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility.In this paper, we aim at addressing thecurse-of-dimensionalityproblem in LDP-based high-dimensional data collection. Based on the idea of machine learning and data synthesis, we propose DP-Fed-Wae, an efficient privacy-preserving framework for collecting high-dimensional categorical data. With the combination of a generative autoencoder, federated learning, and differential privacy, our framework is capable of privately learning the statistical distributions of local data and generating high utility synthetic data on the server side without revealing users’ private information. We have evaluated the framework in terms of data utility and privacy protection on a number of real-world datasets containing 68–124 classification attributes. We show that our framework outperforms the LDP-based baseline algorithms in capturing joint distributions and correlations of attributes and generating high-utility synthetic data. With a local privacy guarantee ∈ = 8, the machine learning models trained with the synthetic data generated by the baseline algorithm cause an accuracy loss of 10% ~ 30%, whereas the accuracy loss is significantly reduced to less than 3% and at best even less than 1% with our framework. Extensive experimental results demonstrate the capability and efficiency of our framework in synthesizing high-dimensional data while striking a satisfactory utility-privacy balance.