arXiv subjects

Mary Anne Smart

Publications and source records attributed to Mary Anne Smart.

4 recordsLinked to original sources

arXiv2024

Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy

Differential privacy is a popular privacy-enhancing technology that has been deployed both in industry and government agencies. Unfortunately, existing explanations of differential privacy fail to set accurate privacy expectations for data subjects, which depend on the choice of deployment model. We design and evaluate new explanations of differential privacy for the local and central models, drawing inspiration from prior work explaining other privacy-enhancing technologies. We find that consequences-focused explanations in the style of privacy nutrition labels that lay out the implications of differential privacy are a promising approach for setting accurate privacy expectations. Further, we find that while process-focused explanations are not enough to set accurate privacy expectations, combining consequences-focused explanations with a brief description of how differential privacy works leads to greater trust.

cs.CR↗

arXiv2023

What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy

Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by the privacy budget parameter, $ε$. Prior work in health and computational science finds that people struggle to reason about probabilistic risks. Yet, communicating the implications of $ε$ to people contributing their data is vital to avoiding privacy theater -- presenting meaningless privacy protection as meaningful -- and empowering more informed data-sharing decisions. Drawing on best practices in risk communication and usability, we develop three methods to convey probabilistic DP guarantees to end users: two that communicate odds and one offering concrete examples of DP outputs. We quantitatively evaluate these explanation methods in a vignette survey study ($n=963$) via three metrics: objective risk comprehension, subjective privacy understanding of DP guarantees, and self-efficacy. We find that odds-based explanation methods are more effective than (1) output-based methods and (2) state-of-the-art approaches that gloss over information about $ε$. Further, when offered information about $ε$, respondents are more willing to share their data than when presented with a state-of-the-art DP explanation; this willingness to share is sensitive to $ε$ values: as privacy protections weaken, respondents are less likely to share data.

cs.CR↗

arXiv2021

Addressing Privacy Threats from Machine Learning

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.

cs.CY↗

arXiv2021

Approximate Data Deletion from Machine Learning Models

Deleting data from a trained machine learning (ML) model is a critical task in many applications. For example, we may want to remove the influence of training points that might be out of date or outliers. Regulations such as EU's General Data Protection Regulation also stipulate that individuals can request to have their data deleted. The naive approach to data deletion is to retrain the ML model on the remaining data, but this is too time consuming. In this work, we propose a new approximate deletion method for linear and logistic models whose computational cost is linear in the the feature dimension $d$ and independent of the number of training data $n$. This is a significant gain over all existing methods, which all have superlinear time dependence on the dimension. We also develop a new feature-injection test to evaluate the thoroughness of data deletion from ML models.

cs.LG↗