arXiv subjects

Boel Nelson

Publications and source records attributed to Boel Nelson.

9 recordsLinked to original sources

arXiv2025

User Perceptions and Attitudes Toward Untraceability in Messaging Platforms

Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as password-protected chats and end-to-end encryption, which primarily protect message contents. Beyond contents, a lot can be inferred about people simply by tracing who sends and receives messages, when, and how often. This paper explores user perceptions of and attitudes toward "untraceability", defined as preventing third parties from tracing who communicates with whom, to inform the design of privacy-enhancing technologies and untraceable communication protocols. Through a vignette-based qualitative study with 189 participants, we identify a diverse set of features that users perceive to be useful for untraceable messaging, ranging from using aliases instead of real names to VPNs. Through a reflexive thematic analysis, we uncover three overarching attitudes that influence the support or rejection of untraceability in messaging platforms and that can serve as a set of new privacy personas: privacy fundamentalists, who advocate for privacy as a universal right; safety fundamentalists, who support surveillance for the sake of accountability; and optimists, who advocate for privacy in principle but also endorse exceptions in idealistic ways, such as encryption backdoors. We highlight a critical gap between the threat models assumed by users and those addressed by untraceable communication protocols. Many participants understood untraceability as a form of anonymity, but interpret it as senders and receivers hiding their identities from each other, rather than from external network observers. We discuss implications for design of strategic communication and user interfaces of untraceable messaging protocols, and propose framing untraceability as a form of "altruistic privacy", i.e., adopting privacy-enhancing technologies to protect others, as a promising strategy to foster broad adoption.

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arXiv2025

Private Lossless Multiple Release

Koufogiannis et al. (2016) showed a $\textit{gradual release}$ result for Laplace noise-based differentially private mechanisms: given an $\varepsilon$-DP release, a new release with privacy parameter $\varepsilon' > \varepsilon$ can be computed such that the combined privacy loss of both releases is at most $\varepsilon'$ and the distribution of the latter is the same as a single release with parameter $\varepsilon'$. They also showed gradual release techniques for Gaussian noise, later also explored by Whitehouse et al. (2022). In this paper, we consider a more general $\textit{multiple release}$ setting in which analysts hold private releases with different privacy parameters corresponding to different access/trust levels. These releases are determined one by one, with privacy parameters in arbitrary order. A multiple release is $\textit{lossless}$ if having access to a subset $S$ of the releases has the same privacy guarantee as the least private release in $S$, and each release has the same distribution as a single release with the same privacy parameter. Our main result is that lossless multiple release is possible for a large class of additive noise mechanisms. For the Gaussian mechanism we give a simple method for lossless multiple release with a short, self-contained analysis that does not require knowledge of the mathematics of Brownian motion. We also present lossless multiple release for the Laplace and Poisson mechanisms. Finally, we consider how to efficiently do gradual release of sparse histograms, and present a mechanism with running time independent of the number of dimensions.

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arXiv2023

PLAN: Variance-Aware Private Mean Estimation

Differentially private mean estimation is an important building block in privacy-preserving algorithms for data analysis and machine learning. Though the trade-off between privacy and utility is well understood in the worst case, many datasets exhibit structure that could potentially be exploited to yield better algorithms. In this paper we present $\textit{Private Limit Adapted Noise}$ (PLAN), a family of differentially private algorithms for mean estimation in the setting where inputs are independently sampled from a distribution $\mathcal{D}$ over $\mathbf{R}^d$, with coordinate-wise standard deviations $\boldsymbol{\sigma} \in \mathbf{R}^d$. Similar to mean estimation under Mahalanobis distance, PLAN tailors the shape of the noise to the shape of the data, but unlike previous algorithms the privacy budget is spent non-uniformly over the coordinates. Under a concentration assumption on $\mathcal{D}$, we show how to exploit skew in the vector $\boldsymbol{\sigma}$, obtaining a (zero-concentrated) differentially private mean estimate with $\ell_2$ error proportional to $\|\boldsymbol{\sigma}\|_1$. Previous work has either not taken $\boldsymbol{\sigma}$ into account, or measured error in Mahalanobis distance $\unicode{x2013}$ in both cases resulting in $\ell_2$ error proportional to $\sqrt{d}\|\boldsymbol{\sigma}\|_2$, which can be up to a factor $\sqrt{d}$ larger. To verify the effectiveness of PLAN, we empirically evaluate accuracy on both synthetic and real world data.

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arXiv2023

Differentially Private Selection from Secure Distributed Computing

Given a collection of vectors $x^{(1)},\dots,x^{(n)} \in \{0,1\}^d$, the selection problem asks to report the index of an "approximately largest" entry in $x=\sum_{j=1}^n x^{(j)}$. Selection abstracts a host of problems--in machine learning it can be used for hyperparameter tuning, feature selection, or to model empirical risk minimization. We study selection under differential privacy, where a released index guarantees privacy for each vectors. Though selection can be solved with an excellent utility guarantee in the central model of differential privacy, the distributed setting lacks solutions. Specifically, strong privacy guarantees with high utility are offered in high trust settings, but not in low trust settings. For example, in the popular shuffle model of distributed differential privacy, there are strong lower bounds suggesting that the utility of the central model cannot be obtained. In this paper we design a protocol for differentially private selection in a trust setting similar to the shuffle model--with the crucial difference that our protocol tolerates corrupted servers while maintaining privacy. Our protocol uses techniques from secure multi-party computation (MPC) to implement a protocol that: (i) has utility on par with the best mechanisms in the central model, (ii) scales to large, distributed collections of high-dimensional vectors, and (iii) uses $k\geq 3$ servers that collaborate to compute the result, where the differential privacy holds assuming an honest majority. Since general-purpose MPC techniques are not sufficiently scalable, we propose a novel application of integer secret sharing, and evaluate the utility and efficiency of our protocol theoretically and empirically. Our protocol is the first to demonstrate that large-scale differentially private selection is possible in a distributed setting.

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arXiv2022

Metadata Privacy Beyond Tunneling for Instant Messaging

Transport layer data leaks metadata unintentionally -- such as who communicates with whom. While tools for strong transport layer privacy exist, they have adoption obstacles, including performance overheads incompatible with mobile devices. We posit that by changing the objective of metadata privacy for $\textit{all traffic}$, we can open up a new design space for pragmatic approaches to transport layer privacy. As a first step in this direction, we propose using techniques from information flow control and present a principled approach to constructing formal models of systems with metadata privacy for $\textit{some}$, deniable, traffic. We prove that deniable traffic achieves metadata privacy against strong adversaries -- this constitutes the first bridging of information flow control and anonymous communication to our knowledge. Additionally, we show that existing state-of-the-art protocols can be extended to support metadata privacy, by designing a novel protocol for $\textit{deniable instant messaging}$ (DenIM), which is a variant of the Signal protocol. To show the efficacy of our approach, we implement and evaluate a proof-of-concept instant messaging system running DenIM on top of unmodified Signal. We empirically show that the DenIM on Signal can maintain low-latency for unmodified Signal traffic without breaking existing features, while at the same time supporting deniable Signal traffic.

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arXiv2022

With a Little Help from My Friends: Transport Deniability for Instant Messaging

Traffic analysis for instant messaging (IM) applications continues to pose an important privacy challenge. In particular, transport-level data can leak unintentional information about IM -- such as who communicates with whom. Existing tools for metadata privacy have adoption obstacles, including the risks of being scrutinized for having a particular app installed, and performance overheads incompatible with mobile devices. We posit that resilience to traffic analysis must be directly supported by major IM services themselves, and must be done in a low-cost manner without breaking existing features. As a first step in this direction, we propose a hybrid messaging model that combines regular and deniable messages. We present a novel protocol for deniable instant messaging, which we call DenIM. DenIM is built on the principle that deniable messages can be made indistinguishable from regular messages with a little help from a user's friends. Deniable messages' network traffic can then be explained by a plausible cover story. DenIM achieves overhead proportional to the messages sent, as opposed to scaling with time or number of users. To show the effectiveness of DenIM, we implement a trace simulator, and show that DenIM's deniability guarantees hold against strong adversaries such as internet service providers.

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arXiv2021

Efficient Error Prediction for Differentially Private Algorithms

Differential privacy is a strong mathematical notion of privacy. Still, a prominent challenge when using differential privacy in real data collection is understanding and counteracting the accuracy loss that differential privacy imposes. As such, the accuracy/privacy trade-off of differential privacy needs to be balanced on a case-by-case basis. Applications in the literature tend to focus solely on analytical accuracy bounds, not include data in error prediction, or use arbitrary settings to measure error empirically. To fill the gap in the literature, we propose a novel application of factor experiments to create data aware error predictions. Basically, factor experiments provide a systematic approach to conducting empirical experiments. To demonstrate our methodology in action, we conduct a case study where error is dependent on arbitrarily complex tree structures. We first construct a tool to simulate poll data. Next, we use our simulated data to construct a least squares model to predict error. Last, we show how to validate the model. Consequently, our contribution is a method for constructing error prediction models that are data aware.

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arXiv2021

Randori: Local Differential Privacy for All

Polls are a common way of collecting data, including product reviews and feedback forms. However, few data collectors give upfront privacy guarantees. Additionally, when privacy guarantees are given upfront, they are often vague claims about 'anonymity'. Instead, we propose giving quantifiable privacy guarantees through the statistical notion of differential privacy. Nevertheless, privacy does not come for free. At the heart of differential privacy lies an inherent trade-off between accuracy and privacy that needs to be balanced. Thus, it is vital to properly adjust the accuracy-privacy trade-off before setting out to collect data. Altogether, getting started with differentially private data collection can be challenging. Ideally, a data analyst should not have to be concerned about all the details of differential privacy, but rather get differential privacy by design. Still, to the best of our knowledge, no tools for gathering poll data under differential privacy exists. Motivated by the lack of tools to gather poll data under differential privacy, we set out to engineer our own tool. Specifically, to make local differential privacy accessible for all, in this systems paper we present Randori, a set of novel open source tools for differentially private poll data collection. Randori is intended to help data analysts keep their focus on what data their poll is collecting, as opposed to how they should collect it. Our tools also allow the data analysts to analytically predict the accuracy of their poll. Furthermore, we show that differential privacy alone is not enough to achieve end-to-end privacy in a server-client setting. Consequently, we also investigate and mitigate implicit data leaks in Randori.

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arXiv2019

SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication

Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations. Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade off and how to address it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy.

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