bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinematic Effects of the Target on the Velocity of Taekwon-Do Roundhouse Kicks
Authors: Wąsik Jacek , Mosler Dariusz , Ortenburger Dorota , Góra Tomasz , Cholewa Jarosław
Published: November 2021

The phenomena of target kinematic effects under different striking conditions and applying different techniques constitute one of the fields of research for sports biomechanics. However, the influence of some kinematic variables which change under different strike conditions for specific parts of the lower limb remains unknown. The aim of this study was to extend the knowledge on how targets of different shapes or the lack of a physical target would affect maximal velocity registered by a marker placed on the foot, knee and hip during the execution of a roundhouse kick. In total, 15 adult males were included in this study. All participants were taekwon-do elite athletes. The displacement of markers placed on the lateral side of the foot, knee and hip during movement execution was registered by a stereophotogrammetry apparatus. Participants performed taekwon-do roundhouse kicks for three target types (into the air, a table tennis ball and a training shield) applying either a sport or a traditional style. The highest maximal velocity was obtained for kicking into the training shield. When applying the sport style, the highest maximal velocity of foot markers for the executed kicks was registered. Kicking into air resulted in higher velocities for proximal body parts than kicking into a tennis ball, but the effect was reversed for the foot marker. In conclusion, a large resistance target is suitable for athletes’ motor preparation as it allows the highest maximum velocity to be reached. Small non-resistant targets are recommended for technical training.
Comparison of the Ramp and Step Incremental Exercise Test Protocols in Assessing the Maximal Fat Oxidation Rate in Youth Cyclists
Authors: Michalik Kamil , Danek Natalia , Zatoń Marek
Published: November 2021

The incremental exercise test is the most common method in assessing the maximal fat oxidation (MFO) rate. The main aim of the study was to determine whether the progressive linear RAMP test can be used to assess the maximal fat oxidation rate along with the intensities that trigger its maximal (FATmax) and its minimal (FATmin) values. Our study comprised 57 young road cyclists who were tested in random order. Each of them was submitted to two incremental exercise tests on an electro-magnetically braked cycle-ergometer - STEP (50 W·3 min-1) and RAMP (~0.278 W·s-1) at a 7-day interval. A stoichiometric equation was used to calculate the fat oxidation rate, while the metabolic thresholds were defined by analyzing ventilation gases. The Student’s T-test, Bland-Altman plots and Pearson’s linear correlations were resorted to in the process of statistical analysis. No statistically significant MFO variances occurred between the tests (p = 0.12) and its rate amounted to 0.57 ± 0.15 g·min-1 and 0.53 ± 0.17 g·min-1 in the STEP and RAMP, respectively. No statistically significant variances in the absolute and relative (to maximal) values of oxygen uptake and heart rate were discerned at the FATmax and FATmin intensities. The RAMP test displayed very strong oxygen uptake correlations between the aerobic threshold and FATmax (r = 0.93, R2 = 0.87, p < 0.001) as well as the anaerobic threshold and FATmin (r = 0.88, R2 = 0.78, p < 0.001). Our results corroborate our hypothesis that the incremental RAMP test as well as the STEP test are reliable tools in assessing MFO, FATmax and FATmin intensities.
Impact of Movement Tempo Distribution on Bar Velocity During a Multi-Set Bench Press Exercise
Authors: Trybulski Robert , Jarosz Jakub , Krzysztofik Michal , Filip-Stachnik Aleksandra , Matykiewicz Patryk , Zmijewski Piotr , Bichowska Marta , Wilk Michal
Published: November 2021

The goal of the present study was to evaluate the effect of contrast tempo movement on bar velocity changes during a multi-set bench press exercise. In randomized and counter-balanced order, participants performed three sets of the bench press exercise at 60%1RM under two testing conditions: E-E where all repetitions were performed with explosive (X/0/X/0) movement tempo; and S-E where the first two repetitions were performed with a slow tempo (5/0/X/0) while the third repetition was performed with explosive movement tempo (slow, slow, explosive). Twelve healthy men volunteered for the study (age = 30 ± 5 years; body mass = 88 ± 10 kg; bench press 1RM = 145 ± 24 kg). The three-way repeated measures ANOVA (tempo × set × repetition) showed statistically significant multi-interaction effect for peak bar velocity (p < 0.01; η2 = 0.23), yet not for mean bar velocity (p = 0.09; η2 = 0.14). The post hoc results for multi-interaction revealed that peak bar velocity in the 3rd repetition was significantly higher for E-E compared to SE only during set 1 (p < 0.001). Therefore, the distribution of movement tempo had a significant impact on peak bar velocity, but not on mean bar velocity. The decrease in peak bar velocity in the 3rd repetition during the S-E condition was observed only in the first set, while such a tendency was not observed in the second and third set.
The Effect of an Olympic Distance Triathlon on Pulmonary Diffusing Capacity and its Recovery 24 Hours Later
Authors: Boussana Alain , Galy Olivier , Gallais Daniel , Hue Olivier
Published: November 2021

The Olympic distance triathlon includes maximal exercise bouts with transitions between the activities. This study investigated the effect of an Olympic distance triathlon (1.5-km swim, 40-km bike, 10-km run) on pulmonary diffusion capacity (DLCO). In nine male triathletes (age: 24 ± 4.7 years), we measured DLCO and calculated the DLCO to alveolar volume ratio (DLCO/VA) and performed spirometry testing before a triathlon (pre-T), 2 hours after the race (post-T), and the day following the race (post-T-24 h). DLCO was measured using the 9-s breath-holding method. We found that (1) DLCO decreased significantly between pre- and post-T values (38.52 ± 5.44 vs. 35.92 ± 6.63 ml∙min-1∙mmHg-1) (p < 0.01) and returned to baseline at post-T-24 h (38.52 ± 5.44 vs. 37.24 ± 6.76 ml∙min-1∙mmHg-1, p > 0.05); (2) DLCO/VA was similar at the pre-, post- and post-T-24 h DLCO comparisons; and (3) forced expiratory volume in the first second (FEV1) and mean forced expiratory flow during the middle half of vital capacity (FEF25-75%) significantly decreased between pre- and post-T and between pre- and post-T-24-h (p < 0.02). In conclusion, a significant reduction in DLCO and DLCO/VA 2 hours after the triathlon suggests the presence of pulmonary interstitial oedema. Both values returned to baseline 24 hours after the race, which reflects possible mild and transient pulmonary oedema with minimal physiological significance.
Differences in Nervous Autonomic Control in Response to a Single Session of Exercise in Bodybuilders Using Anabolic Androgenic Steroids
Authors: Júnior Jefferson , Silva Alexandre , Cardoso Glêbia , Silvino Valmir , Veras-Silva Acácio , Nakamura Fabio , Santos Marcos
Published: November 2021

Considering the role of autonomic nerve activity in blood pressure control, this study aimed to investigate the cardiac autonomic nerve responses after an aerobic exercise session in Anabolic Androgenic Steroids (AAS) users. Twenty men (AAS, n = 9; control group, n = 11) performed an aerobic exercise session (60 min, 70 to 80% of HRmax). Heart rate variability (HRV) was assessed before and during a 60-min post-exercise recovery period. RMSSD (root mean square successive difference of the RR intervals) and the LF/HF ratio (low frequency/high frequency spectra) were also evaluated. The Student's t-test for independent samples was used to compare differences between initial group characteristics. Repeated measures ANOVA was used to compare pre- and post-exercise HRV recovery (p < 0.05). AAS had a lower SDNN (standard deviation of the intervals) (40.8 ± 16.8 vs. 71.6 ± 24.7 ms; p = 0.04, d = 1.4) and a higher LF/HF (3.4 ± 2.1 vs. 1.8 ± 0.9%; p = 0.03, d = 0.9) before exercise. AAS and controls had similar RMSSD (14.0 ± 15.8 vs. 18.9 ± 12.1 ms; p = 0.20) and a LF/HF (2.8 ± 1.6 vs. 2.4 ± 1.2 ms; p = 0.41) immediately post-exercise. The between-groups comparison revealed a higher HF/LF at 30 min (4.3 ± 1.4 vs. 2.5 ± 1.3%; p = 0.008, d = 1.3) and 60 min (5.0 ± 2.2 vs. 2.3 ± 0.8%; p = 0.001, d = 1.6) for the AAS group in the recovery time. This study demonstrated impaired parasympathetic activity at rest and immediately after the exercise session as an adverse effect of AAS usage, but similar behavior regarding the restoration of sympathetic activity.
Comparison of Muscle Activity During 200 m Indoor Curve and Straight Sprinting in Elite Female Sprinters
Authors: Pietraszewski Przemysław , Gołaś Artur , Krzysztofik Michał
Published: November 2021

The purpose of this study was to assess whether peak surface electromyography (sEMG) amplitude of selected lower limb muscles differed during a) curve and straight sprinting, b) sprinting in inside and outside lanes between lower limbs. Eleven well-trained female sprinters (personal best: 24.1 ± 1.1 s) were included in a randomized within-subject design study, in which participants underwent two experimental conditions: all-out 200 m indoor sprints in the innermost and outermost lane. Peak sEMG amplitude was recorded bilaterally from gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, and vastus lateralis muscles. Left gastrocnemius medialis peak sEMG amplitude was significantly higher than for the right leg muscle during curve (p = 0.011) and straight sprinting (p < 0.001) when sprinting in the inside lane, and also significantly higher when sprinting in the inside vs. outside lane for both curve and straight sprinting (p = 0.037 and p = 0.027, respectively). Moreover, left biceps femoris peak sEMG amplitude was significantly higher during straight sprinting in the inside vs. outside lane (p = 0.006). Furthermore, right and left vastus lateralis peak sEMG amplitude was significantly higher during curve sprinting in the inside lane (p = 0.001 and p = 0.004, respectively) and for the left leg muscle peak sEMG amplitude was significantly higher during curve compared to straight sprinting in the outside lane (p = 0.024). Results indicate that curve sprinting creates greater demands mainly for the gastrocnemius medialis of the inner than the outer leg, but the degree of these requirements seems to depend on the radius of the curve, thus significant changes were noted during sprinting in the inside lane, but not in the outside lane.
Mutual inductance of two coaxial rectangular PCB coils incorporating magnetic layer
Authors: Bendimerad Salah-Eddine , Baghli Selma , Ayad Abdelghani , Tilmatine Amar
Published: November 2021

The wireless connection distance between the transmission and reception coils of printed circuit boards (PCB) influences the mutual inductance and affects circuit performance. In this study, the mutual inductance M of PCB coils was investigated, and two analytical methods were presented for calculating the mutual inductance between two coaxial rectangular planar PCB coils incorporating magnetic layer. The results were acquired through calculations by using the Neumann integral and Biot-Savart methods. The complete integral calculations and detailed demonstrations of the two methods are presented. The obtained formulas were introduced in some examples of coils with different number of turns. The analytical and experimental results were compared, and a strong agreement between them was observed.
Estimation of parameters of a sum of sinusoids sampled below the Nyquist rate with frequencies away from the grid
Authors: Djurović Igor
Published: November 2021

We are witnessing a growing interest in processing signals sampled below the Nyquist rate. The main limitation of current approaches considering estimation of multicomponent sinusoids parameters is the assumption of frequencies on the frequency grid. The sinusoids away from the frequency grid are considered in this paper. The proposed procedure has three stages. In the first two, a rough estimation of signal components is performed while in the third refinement in estimation is achieved in a component-by-component manner. We have tested the developed technique on an extended set of simulation examples showing excellent accuracy. Three scenarios are considered in experiments: missing samples, noisy environment, and non-uniform sampling below the Nyquist rate.
A novel compact stair-shaped multiband fractal antenna for wireless communication systems
Authors: Jaffri Zain ul Abidin , Ahmad Zeeshan , Kabir Asif , Bukhari S. Sabahat H.
Published: November 2021

The growing demand for enhanced capacities, broadband services, and high transmission speeds to accommodate speech, image, multimedia, and data communication simultaneously puts a requirement for antenna to operate in multiple frequency bands. A novel compact fractal antenna based on self-similar stair-shaped fractal geometry is proposed in this paper. The fractal antenna is designed by modifying the patch antenna through the iterative process using stair-shaped fractal geometry. The third iteration results in a tri-band response, and the antenna resonate at 3.65, 4.825, and 6.325 GHz with impedance bandwidths of 75.6, 121.2, and 211.4 MHz, respectively. The antenna is designed in CST Microwave studio, and evaluated for operating bands and radiation characteristics. Prototype for the third iteration of the fractal antenna is fabricated on FR-4 substrate which is further tested for measured operating bands and radiation characteristics. The simulated and measured results show good agreement.
Fault detection in power grids based on improved supervised machine learning binary classification
Authors: Wadi Mohammed
Published: November 2021

With the increased complexity of power systems and the high integration of smart meters, advanced sensors, and high-level communication infrastructures within the modern power grids, the collected data becomes enormous and requires fast computation and outstanding analyzing methods under normal conditions. However, under abnormal conditions such as faults, the challenges dramatically increase. Such faults require timely and accurate fault detection, identification, and location approaches for guaranteeing their desired performance. This paper proposes two machine learning approaches based on the binary classification to improve the process of fault detection in smart grids. Besides, it presents four machine learning models trained and tested on real and modern fault detection data set designed by the Technical University of Ostrava. Many evaluation measures are applied to test and compare these approaches and models. Moreover, receiver operating characteristic curves are utilized to prove the applicability and validity of the proposed approaches. Finally, the proposed models are compared to previous studies to confirm their superiority.
Wideband ring mixer for band #1 of MB-OFDM systems in 180 nm CMOS technology
Authors: Chaturvedi Abhay , Kumar Mithilesh , Meena Ram Swaroop , Sharma Gaurav Kumar
Published: November 2021

A wideband down conversion ring mixer is proposed for multi band orthogonal frequency division multiplexing (MB-OFDM) system in 180 nm CMOS technology. The mixer is essentially used in a heterodyne wireless receiver to enhance the selectivity of the system. Being a nonlinear system, the mixer dominates the overall performance of the system. The design of down conversion mixer is the most challenging part of a receive chain. Wideband impedance matching always remains a challenge in any radio frequency integrated circuit design. This paper presents the design of a ring mixer with high linearity, wideband impedance matching using differential resistive impedance matching and without using any DC bias. The proposed mixer is tuned for a frequency of 3.432 GHz of band 1 of the MB-OFDM system. Mixer core is based on the FET ring mixer topology. The mixer is implemented in 180 nm CMOS technology. The mixer achieves the minimum conversion loss of 10.49 dB, 1 dB compression point (P1) of 12.40 dBm, third order input intercept point (IIP3) of 12.01 dBm, a minimum SSB noise figure of 8.99 dB, andS11of less than -10 dB over the frequency range of 0 to 13.61 GHz . The layout of the mixer records an active area of 183.75μm2.
Energy-efficient MAC protocol for wireless LANs with WiFi sensors
Authors: Choi Woo-Yong
Published: November 2021

More and more mobile computing devices such as smartphones with limited battery power are being used in IEEE 802.11 wireless LANs, and WiFi sensors with very limited battery power are expected to get Internet access through wireless LANs in the near future. We propose an efficient MAC (Medium Access Control) protocol so that WiFi sensors and mobile devices are connected to APs (Access Points) in IEEE 802.11 wireless LANs in an energy-efficient manner.
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

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

This 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.
The Future of Judging: Algorithms in the Courtroom
Authors: Levy Inbar
Published: November 2021

These keynote lecture notes explore the potential and limitations of using algorithms and machine learning tools in the courtroom. It identifies different areas in which algorithms would be suitable for legal decision-making and areas in which human judgments should be preserved.
Understanding the effects of BIM implementation in corporation finance: An empirical study in China
Authors: Wang Zhimin , Ma Jianjun
Published: November 2021

Building 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

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

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

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

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

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

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

In 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

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

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

Consider 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

Fair 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

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

Business 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.
Masking Feedforward Neural Networks Against Power Analysis Attacks
Authors: Athanasiou Konstantinos , Wahl Thomas , Ding A. Adam , Fei Yunsi
Published: November 2021

Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.
From “Onion Not Found” to Guard Discovery
Authors: Oldenburg Lennart , Acar Gunes , Diaz Claudia
Published: November 2021

We present a novel web-based attack that identifies a Tor user’s guard in a matter of seconds. Our attack is low-cost, fast, and stealthy. It requires only a moderate amount of resources and can be deployed by website owners, third-party script providers, and malicious exits—if the website traffic is unencrypted. The attack works by injecting resources from non-existing onion service addresses into a webpage. Upon visiting the attack webpage with Tor Browser, the victim’s Tor client creates many circuits to look up the non-existing addresses. This allows middle relays controlled by the adversary to detect the distinctive traffic pattern of the “404 Not Found” lookups and identify the victim’s guard. We evaluate our attack with extensive simulations and live Tor network measurements, taking a range of victim machine, network, and geolocation configurations into account. We find that an adversary running a small number of HSDirs and providing 5 % of Tor’s relay bandwidth needs 12.06 seconds to identify the guards of 50 % of the victims, while it takes 22.01 seconds to discover 90 % of the victims’ guards. Finally, we evaluate a set of countermeasures against our attack including a defense that we develop based on a token bucket and the recently proposed Vanguards-lite defense in Tor.
DataProVe: Fully Automated Conformance Verification Between Data Protection Policies and System Architectures
Authors: Ta Vinh Thong , Eiza Max Hashem
Published: November 2021

Privacy and data protection by design are relevant parts of the General Data Protection Regulation (GDPR), in which businesses and organisations are encouraged to implement measures at an early stage of the system design phase to fulfil data protection requirements. This paper addresses the policy and system architecture design and propose two variants of privacy policy language and architecture description language, respectively, for specifying and verifying data protection and privacy requirements. In addition, we develop a fully automated algorithm based on logic, for verifying three types of conformance relations (privacy, data protection, and functional conformance) between a policy and an architecture specified in our languages’ variants. Compared to related works, this approach supports a more systematic and fine-grained analysis of the privacy, data protection, and functional properties of a system. Our theoretical methods are then implemented as a software tool called DataProVe and its feasibility is demonstrated based on the centralised and decentralised approaches of COVID-19 contact tracing applications.
Setting the Bar Low: Are Websites Complying With the Minimum Requirements of the CCPA?
Authors: Van Nortwick Maggie , Wilson Christo
Published: November 2021

On June 28, 2018, the California State Legislature passed the California Consumer Privacy Act (CCPA), arguably the most comprehensive piece of online privacy legislation in the United States. Online services covered by the CCPA are required to provide a hyperlink on their homepage with the text “Do Not Sell My Personal Information” (DNSMPI). The CCPA went into effect on January 1, 2020, a date that was chosen to give data collectors time to study the new law and bring themselves into compliance.

In this study, we begin the process of investigating whether websites are complying with the CCPA by focusing on DNSMPI links. Using longitudinal data crawled from the top 1M websites in the Tranco ranking, we examine which websites are including DNSMPI links, whether the websites without DNSMPI links are out of compliance with the law, whether websites are using geofences to dynamically hide DNSMPI links from non-Californians, how DNSMPI adoption has changed over time, and how websites are choosing to present DNSMPI links (e.g., in terms of font size, color, and placement). We argue that the answers to these questions are critical for spurring enforcement actions under the law, and helping to shape future privacy laws and regulations, e.g., rule making that will soon commence around the successor to the CCPA, known as the CPRA.
Unbound: Ethics, Law, Sustainability, and the New Space Race
Authors: Impey Chris
Published: November 2021

We are witnessing a new space race. A half century after the last Moon landing, and after a decade during which the United States could not launch its own astronauts to Earth orbit, there is new energy in the space activity. China has huge ambitions to rival or eclipse America as the major space power, and other countries are developing space programs. However, perhaps the greatest excitement attaches to the entrepreneurs who are trying to create a new business model for space travel based initially on tourism, and eventually, on colonizing the Moon and Mars and harvesting resources from asteroids. This paper presents a snapshot of the new space race and the rich men behind it, and it looks at some of the ethical and legal issues raised by this activity. The methodology is to consider the stated ambitions of the men leading private space companies, compare and contrast the space endeavor with earlier episodes of exploration and transportation innovation, review the regulatory environment for outer space, and consider two divergent scenarios for the future. Opinions are divided on whether commercial space flight is an expensive indulgence or potentially a way to find sustainability solutions for our life on Earth. It is concluded that the new space race can be characterized as unbounded: in ambition, in terms of laws and regulations, and in terms of ethical constraints on the activity.