bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personal information inference from voice recordings: User awareness and privacy concerns
Authors: Kröger Jacob Leon , Gellrich Leon , Pape Sebastian , Brause Saba Rebecca , Ullrich Stefan 
Published: November 2021

AbstractThrough voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. g., geographical origin, health status, personality). We conducted a nationally representative survey in the UK (n = 683, 18–69 years) to investigate people’s awareness about the inferential power of voice and speech analysis. Our results show that – while awareness levels vary between different categories of inferred information – there is generally low awareness across all participant demographics, even among participants with professional experience in computer science, data mining, and IT security. For instance, only 18.7% of participants are at least somewhat aware that physical and mental health information can be inferred from voice recordings. Many participants have rarely (28.4%) or never (42.5%) even thought about the possibility of personal information being inferred from speech data. After a short educational video on the topic, participants express only moderate privacy concern. However, based on an analysis of open text responses, unconcerned reactions seem to be largely explained by knowledge gaps about possible data misuses. Watching the educational video lowered participants’ intention to use voice-enabled devices. In discussing the regulatory implications of our findings, we challenge the notion of “informed consent” to data processing. We also argue that inferences about individuals need to be legally recognized as personal data and protected accordingly.
(∈, δ)-Indistinguishable Mixing for Cryptocurrencies
Authors: Liang Mingyu , Karantaidou Ioanna , Baldimtsi Foteini , Gordon S. Dov , Varia Mayank 
Published: November 2021

AbstractWe propose a new theoretical approach for building anonymous mixing mechanisms for cryptocurrencies. Rather than requiring a fully uniform permutation during mixing, we relax the requirement, insisting only that neighboring permutations are similarly likely. This is defined formally by borrowing from the definition of differential privacy. This relaxed privacy definition allows us to greatly reduce the amount of interaction and computation in the mixing protocol. Our construction achievesO(n·polylog(n)) computation time for mixingnaddresses, whereas all other mixing schemes requireO(n2) total computation across all parties. Additionally, we support a smooth tolerance of fail-stop adversaries and do not require any trusted setup. We analyze the security of our generic protocol under the UC framework, and under a stand-alone, game-based definition. We finally describe an instantiation using ring signatures and confidential transactions.
How Can and Would People Protect From Online Tracking?
Authors: Mehrnezhad Maryam , Coopamootoo Kovila , Toreini Ehsan 
Published: November 2021

Abstract
Online tracking is complex and users find it challenging to protect themselves from it. While the academic community has extensively studied systems and users for tracking practices, the link between the data protection regulations, websites’ practices of presenting privacy-enhancing technologies (PETs), and how users learn about PETs and practice them is not clear. This paper takes a multidimensional approach to find such a link. We conduct a study to evaluate the 100 top EU websites, where we find that information about PETs is provided far beyond the cookie notice. We also find that opting-out from privacy settings is not as easy as opting-in and becomes even more difficult (if not impossible) when the user decides to opt-out of previously accepted privacy settings. In addition, we conduct an online survey with 614 participants across three countries (UK, France, Germany) to gain a broad understanding of users’ tracking protection practices. We find that users mostly learn about PETs for tracking protection via their own research or with the help of family and friends. We find a disparity between what websites offer as tracking protection and the ways individuals report to do so. Observing such a disparity sheds light on why current policies and practices are ineffective in supporting the use of PETs by users.
Towards Improving Code Stylometry Analysis in Underground Forums
Authors: Tereszkowski-Kaminski Michal , Pastrana Sergio , Blasco Jorge , Suarez-Tangil Guillermo 
Published: November 2021

Abstract
Code Stylometry has emerged as a powerful mechanism to identify programmers. While there have been significant advances in the field, existing mechanisms underperform in challenging domains. One such domain is studying the provenance of code shared in underground forums, where code posts tend to have small or incomplete source code fragments. This paper proposes a method designed to deal with the idiosyncrasies of code snippets shared in these forums. Our system fuses a forum-specific learning pipeline with Conformal Prediction to generate predictions with precise confidence levels as a novelty. We see that identifying unreliable code snippets is paramount to generate high-accuracy predictions, and this is a task where traditional learning settings fail. Overall, our method performs as twice as well as the state-of-the-art in a constrained setting with a large number of authors (i.e., 100). When dealing with a smaller number of authors (i.e., 20), it performs at high accuracy (89%). We also evaluate our work on an open-world assumption and see that our method is more effective at retaining samples.
Ulixes: Facial Recognition Privacy with Adversarial Machine Learning
Authors: Cilloni Thomas , Wang Wei , Walter Charles , Fleming Charles 
Published: November 2021

Abstract
Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.
User Perceptions of Gmail’s Confidential Mode
Authors: Qahtani Elham Al , Javed Yousra , Shehab Mohamed 
Published: November 2021

Abstract
Gmail’s confidential mode enables a user to send confidential emails and control access to their content through setting an expiration time and passcode, pre-expiry access revocation, and prevention of email forwarding, downloading, and printing. This paper aims to understand user perceptions and motivations for using Gmail’s confidential mode (GCM). Our structured interviews with 19 Gmail users at UNC Charlotte show that users utilize this mode to share their private documents with recipients and perceive that this mode encrypts their emails and attachments. The most commonly used feature of this mode is the default time expiration of one week, and the least used feature is the pre-expiry access revocation. Our analysis suggests several design improvements.
Making the Most of Parallel Composition in Differential Privacy
Authors: Smith Josh , Asghar Hassan Jameel , Gioiosa Gianpaolo , Mrabet Sirine , Gaspers Serge , Tyler Paul 
Published: November 2021

AbstractWe show that the ‘optimal’ use of the parallel composition theorem corresponds to finding the size of the largest subset of queries that ‘overlap’ on the data domain, a quantity we call themaximum overlapof the queries. It has previously been shown that a certain instance of this problem, formulated in terms of determining the sensitivity of the queries, is NP-hard, but also that it is possible to use graph-theoretic algorithms, such as finding the maximum clique, to approximate query sensitivity. In this paper, we consider a significant generalization of the aforementioned instance which encompasses both a wider range of differentially private mechanisms and a broader class of queries. We show that for a particular class of predicate queries, determining if they are disjoint can be done in time polynomial in the number of attributes. For this class, we show that the maximum overlap problem remains NP-hard as a function of the number of queries. However, we show that efficient approximate solutions exist by relating maximum overlap to the clique and chromatic numbers of a certain graph determined by the queries. The link to chromatic number allows us to use more efficient approximate algorithms, which cannot be done for the clique number as it may underestimate the privacy budget. Our approach is defined in the general setting off-differential privacy, which subsumes standard pure differential privacy and Gaussian differential privacy. We prove the parallel composition theorem forf-differential privacy. We evaluate our approach on synthetic and real-world data sets of queries. We show that the approach can scale to large domain sizes (up to 1020000), and that its application can reduce the noise added to query answers by up to 60%.
Proof-of-Vax: Studying User Preferences and Perception of Covid Vaccination Certificates
Authors: Kowalewski Marvin , Herbert Franziska , Schnitzler Theodor , Dürmuth Markus 
Published: November 2021

Abstract
Digital tools play an important role in fighting the current global COVID-19 pandemic. We conducted a representative online study in Germany on a sample of 599 participants to evaluate the user perception of vaccination certificates. We investigated five different variants of vaccination certificates based on deployed and planned designs in a between-group design, including paper-based and app-based variants. Our main results show that the willingness to use and adopt vaccination certificates is generally high. Overall, paper-based vaccination certificates were favored over app-based solutions. The willingness to use digital apps decreased significantly by a higher disposition to privacy and increased by higher worries about the pandemic and acceptance of the coronavirus vaccination. Vaccination certificates resemble an interesting use case for studying privacy perceptions for health-related data. We hope that our work will educate the currently ongoing design of vaccination certificates, give us deeper insights into the privacy of health-related data and apps, and prepare us for future potential applications of vaccination certificates and health apps in general.
Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF
Authors: Chandran Nishanth , Gupta Divya , Shah Akash 
Published: November 2021

Abstract
In 2-party Circuit-based Private Set Intersection (Circuit-PSI), P
0 and P
1 hold sets S0 and S1 respectively and wish to securely compute a function f over the set S0 ∩ S1 (e.g., cardinality, sum over associated attributes, or threshold intersection). Following a long line of work, Pinkas et al. (PSTY, Eurocrypt 2019) showed how to construct a concretely efficient Circuit-PSI protocol with linear communication complexity. However, their protocol requires super-linear computation.
In this work, we construct concretely efficient Circuit-PSI protocols with linear computational and communication cost. Further, our protocols are more performant than the state-of-the-art, PSTY – we are ≈ 2.3× more communication efficient and are up to 2.8× faster. We obtain our improvements through a new primitive called Relaxed Batch Oblivious Programmable Pseudorandom Functions (RB-OPPRF) that can be seen as a strict generalization of Batch OPPRFs that were used in PSTY. This primitive could be of independent interest.
Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
Authors: Lu Donghang , Yu Albert , Kate Aniket , Maji Hemanta 
Published: November 2021

AbstractWhile the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions.We implement our protocols over the HoneyBadgerMPC library and apply it to two prominent secure computation tasks: privacy-preserving evaluation of decision trees and privacy-preserving evaluation of Markov processes. For the decision tree evaluation problem, we demonstrate the feasibility of evaluating high-depth decision tree models in a generaln-party setting. For the Markov process application, we demonstrate that Poly-math can compute large powers of transition matrices with better online time and less communication.
If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews
Authors: Schäfer Jochen , Müller Christian , Armknecht Frederik 
Published: November 2021

Abstract
Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors are interested into accumulating as many positive reviews (likes) as possible and to make these public. However, we present an attack that exploits these publicly available information to identify cryptocurrency addresses potentially belonging to vendors. In its basic variant, it focuses on vendors that reuse their addresses. We also show an extended variant that copes with the case that addresses are used only once. We demonstrate the applicability of the attack by modeling Bitcoin transactions based on vendor reviews of two separate darknet markets and retrieve matching transactions from the blockchain. By doing so, we can identify Bitcoin addresses likely belonging to darknet market vendors.
Polaris: Transparent Succinct Zero-Knowledge Arguments for R1CS with Efficient Verifier
Authors: Fu Shihui , Gong Guang 
Published: November 2021

AbstractWe present a new zero-knowledge succinct argument of knowledge (zkSNARK) scheme for Rank-1 Constraint Satisfaction (RICS), a widely deployed NP-complete language that generalizes arithmetic circuit satisfiability. By instantiating with different commitment schemes, we obtain several zkSNARKs where the verifier’s costs and the proof size range fromO(log2N) toO(N)O\left( {\sqrt N } \right)depending on the underlying polynomial commitment schemes when applied to anN-gate arithmetic circuit. All these schemes do not require a trusted setup. It is plausibly post-quantum secure when instantiated with a secure collision-resistant hash function. We report on experiments for evaluating the performance of our proposed system. For instance, for verifying a SHA-256 preimage (less than 23k AND gates) in zero-knowledge with 128 bits security, the proof size is less than 150kB and the verification time is less than 11ms, both competitive to existing systems.
SoK: Cryptographic Confidentiality of Data on Mobile Devices
Authors: Zinkus Maximilian , Jois Tushar M. , Green Matthew 
Published: November 2021

Abstract
Mobile devices have become an indispensable component of modern life. Their high storage capacity gives these devices the capability to store vast amounts of sensitive personal data, which makes them a high-value target: these devices are routinely stolen by criminals for data theft, and are increasingly viewed by law enforcement agencies as a valuable source of forensic data. Over the past several years, providers have deployed a number of advanced cryptographic features intended to protect data on mobile devices, even in the strong setting where an attacker has physical access to a device. Many of these techniques draw from the research literature, but have been adapted to this entirely new problem setting.
This involves a number of novel challenges, which are incompletely addressed in the literature. In this work, we outline those challenges, and systematize the known approaches to securing user data against extraction attacks. Our work proposes a methodology that researchers can use to analyze cryptographic data confidentiality for mobile devices. We evaluate the existing literature for securing devices against data extraction adversaries with powerful capabilities including access to devices and to the cloud services they rely on. We then analyze existing mobile device confidentiality measures to identify research areas that have not received proper attention from the community and represent opportunities for future research.
The Effectiveness of Adaptation Methods in Improving User Engagement and Privacy Protection on Social Network Sites
Authors: Namara Moses , Sloan Henry , Knijnenburg Bart P. 
Published: November 2021

AbstractResearch finds that the users of Social Networking Sites (SNSs) often fail to comprehensively engage with the plethora of available privacy features— arguably due to their sheer number and the fact that they are often hidden from sight. As different users are likely interested in engaging with different subsets of privacy features, an SNS could improve privacy management practices by adapting its interface in a way that proactively assists, guides, or prompts users to engage with the subset of privacy features they are most likely to benefit from. Whereas recent work presents algorithmic implementations of such privacy adaptation methods, this study investigates the optimal user interface mechanism to present such adaptations. In particular, we tested three proposed“adaptation methods”(automation, suggestions, highlights) in an online between-subjects user experiment in which 406 participants used a carefully controlled SNS prototype. We systematically evaluate the effect of these adaptation methods on participants’ engagement with the privacy features, their tendency to set stricter settings (protection), and their subjective evaluation of the assigned adaptation method. We find that theautomationof privacy features afforded users the most privacy protection, while giving privacysuggestionscaused the highest level of engagement with the features and the highest subjective ratings (as long as awkward suggestions are avoided). We discuss the practical implications of these findings in the effectiveness of adaptations improving user awareness of, and engagement with, privacy features on social media.
Vibro piles performance prediction using result of CPT
Authors: Więcławski Paweł 
Published: November 2021

Abstract
Vibro piles belong to the group of full displacement piles with an expanded base, characterised by a very high load capacity, especially in non-cohesive soils. The problem is to adopt a reliable method for the determination of full load–settlement (Q–s) curve. A frequent difficulty is the determination of the load capacity limit based on the static load test because the course of the load–settlement curve is of a linear nature. This publication presents the empirical method. It allows direct prediction of a full axially loaded pile settlement curve based on the values of qc cone resistance obtained in cone penetration test (CPT). The advantage offered by this procedure is the accuracy of the obtained limit values in relation to the actual load-bearing capacity as compared to other methods based on soil parameters obtained in in situ testing. An additional advantage is the Q–s characteristics, which enable designing for intermediate values, allowing for the criterion of minimal or equal settlements. The shape of analytical curves was compared with static pile load test (SPLT) curves. This comparison showed large convergences between the analytical and measured curves.
Exploring Perceptions of Religion and Science among Turkish Academics
Authors: Sevinç Kenan , Coleman Thomas J. , Farias Miguel 
Published: November 2021

Abstract
The religiosity of academics has been studied for over a decade. With few exceptions, this research has been conducted on American “elite” scientists, and data from non-Western countries is lacking. Drawing from psychological and sociological literature, the present exploratory study investigates the religiosity of Turkish academics (N = 361) and their perceptions on the relationship between religion and science, and associated variables such as interpretation of the Quran, and belief in evolution and creationism. Moreover, we address criticism directed at previous research by probing for different God concepts among believing academics. Although cultural differences can be identified, the results generally support the idea that academics are less religious with 54% identifying as “less religious” or “not religious,” compared to 24.2% self-identifying as “religious” or “extremely religious.”
Reconciling the Irreconcilable: A Property Rights Approach to Resolving the Animal Rights Debate
Authors: Cesario Anthony J. 
Published: November 2021

Abstract
Libertarianism is understood to be a “deontological theory of law” that purportedly applies exclusively to humans. According to some libertarians, however, “one of the greatest weaknesses of libertarian theory” is that there are no provisions outlawing the abuse and torture of animals even though this seems to be one of “the most heinous acts it is possible to do”. Moreover, a few of these libertarians go even further and claim that this legal philosophy of non-aggression should actually be extended to include other animals. The purpose of this paper is to reconcile this seemingly irreconcilable situation by arguing that it is a “continuum problem” and offering a principled, libertarian compromise that resolves the animal rights debate using the non-aggression principle (NAP) and private property rights.
Rejoinder to Huemer on Animal Rights
Authors: Block Walter E. 
Published: November 2021

Abstract
Heumer and I debate animal rights, utilitarianism, libertarianism, morality and philosophy. We agree that suffering is a problem, and diverge, widely, on how to deal with it. I maintain that this author’s reputation as a libertarian, let alone an intellectual leader of this movement, is problematic. Why? That is because libertarianism, properly understood, is a theory of intra-human rights; this philosophy says nothing about right from an extra-human perspective, Heumer to the contrary notwithstanding. That is to say, he is improperly importing into the freedom philosophy considerations extraneous to it.