Differential Privacy: An Economic Method for Choosing Epsilon (1402.3329v1)

Abstract: Differential privacy is becoming a gold standard for privacy research; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of interesting problems. However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter $\epsilon$, which controls the crucial tradeoff between the strength of the privacy guarantee and the accuracy of the published results. In this paper, we examine the role that these parameters play in concrete applications, identifying the key questions that must be addressed when choosing specific values. This choice requires balancing the interests of two different parties: the data analyst and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose $\epsilon$ on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.

• The paper introduces an economic model that quantitatively balances privacy risks against data utility for selecting epsilon in differential privacy.
• Key findings reveal that differentially private studies can sometimes achieve higher accuracy than non-private ones by lowering compensation demands.
• Case studies in clinical, educational, and social contexts validate the model's practical implications for cost-effective privacy-preserving research.

A Formal Analysis of "Differential Privacy: An Economic Method for Choosing Epsilon"

This paper presents a novel approach to the crucial task of selecting the privacy parameter $\epsilon$ in differential privacy frameworks. Notably, differential privacy has become a standard for preserving privacy in the release of query results by guaranteeing a bound on potential privacy losses, irrespective of the prior knowledge an adversary might possess. Despite the existence of various differential privacy algorithms, the process of effectively choosing $\epsilon$ remains under-researched. This paper addresses this gap by proposing an economic model that aids in determining $\epsilon$ based on real-world parameters and situations.

Key Contributions

1. Model Introduction and Problem Definition:
   • The paper introduces a principled economic model wherein prospective participants assess potential privacy risks against expected benefits, notably focusing on calculating acceptable values for $\epsilon$ and $\delta$ through a calibrated balance between privacy guarantees and data utility.
   • A formal approach is presented for choosing $\epsilon$ in both $\epsilon$ and $(\epsilon, \delta)$-differential privacy scenarios, providing a clear framework that integrates expected cost calculations and participant compensation strategies.
2. Surprising Insights about Differential Privacy:
   • One intriguing finding highlights scenarios where differentially private studies may achieve greater accuracy than non-private settings at an equivalent cost. This result arises from reduced individual risk and compensation demands, counteracting the noise-induced data inaccuracy inherent to privacy-preserving techniques.
3. Case Studies and Comparative Analysis:
   • By employing diverse case studies such as clinical trials, educational data assessment, movie viewing habits, and social network engagement, the authors systematically apply their model to establish varying levels of participant compensation and paper feasibility.
   • Furthermore, they explore the economic implications when a private paper offers a cost advantage over a non-private paper by theoretically reducing the exposure risk. This implication posits a groundbreaking perspective on privacy-preserving methodologies' cost-effectiveness in broader research settings.

Practical Implications

The theoretical constructs articulated in this paper can significantly impact real-world applications by providing data analysts with a tangible mechanism to justify the choice of $\epsilon$ based on concrete economic incentives and risks. By doing so, it aims to alleviate the uncertainties associated with privacy-utility trade-offs in scientific studies and data-driven deployments. Additionally, the integration of realistic scenarios like smoking habits and educational records into their model underscores the potential for widespread application across diverse domains, where privacy concerns are particularly pronounced.

Theoretical Implications and Speculations

The model proposed not only delivers robust guidelines for $\epsilon$-selection but also nudges future differential privacy research towards integrating empirical attacks and coalitional behaviors. By exploring these dimensions, future efforts might address whether current theoretical limits on $\epsilon$'s guarantees mirror real-world adversary attempts. Furthermore, the examination of heterogenous participant costs could yield a dynamic price-discovery approach for data utility within privacy constraints, reminiscent of cryptographic parameter selections against evolving computational capacities.

Conclusions and Future Developments

The model presented in "Differential Privacy: An Economic Method for Choosing Epsilon" provides an innovative contribution to the differential privacy landscape by expanding the choice of $\epsilon$ from an abstract theoretical question to a quantitative, economically grounded decision. As differential privacy continues to gain relevance in data-intensive endeavors, it is anticipated that subsequent iterations of this model will refine methodologies for privacy setting determinations, facilitating enhanced privacy-preserving applications across industries. By addressing both technical and economic aspects, this work sets a precedent for multifaceted privacy analysis, encouraging further scholarly discourse and exploration into economically-informed privacy frameworks.