Correcting Privacy Parameters For County Business Patterns Data

Understanding the County Business Patterns (CBP) Dataset and Differential Privacy

The County Business Patterns (CBP) dataset, a crucial resource for economic analysis, has recently come under scrutiny regarding the accuracy of its privacy parameter descriptions within the OpenDP Deployments Registry. This discussion aims to clarify these discrepancies and propose a more accurate representation of the dataset's privacy mechanisms. Specifically, the current registry entry identifies the CBP data as employing zero-Concentrated Differential Privacy (zCDP) with a privacy unit defined as a business establishment. However, this characterization is inaccurate. Both the U.S. Census Bureau, the source of the CBP data, and the registry entry itself acknowledge that the dataset utilizes per-record differential privacy. This particular variant of DP involves a unique splitting mechanism to ensure privacy, as detailed in academic papers and Census Bureau webinars. This mechanism involves defining thresholds for various attributes of a business establishment, such as the number of employees or total payroll. For instance, a threshold might be set at 100 employees or $50,000 in payroll. Each establishment's data is then evaluated against these thresholds. If an establishment's attribute exceeds any of these predefined limits, the establishment is split into two or more "duplicate" establishments. The values for these split entities are adjusted to ensure that all values fall below the established thresholds. Consider an example: an establishment initially reporting 150 employees and a $60,000 payroll would be divided into two entities. The first might report 100 employees and $50,000 in payroll, while the second reports 50 employees and $10,000 in payroll. Following this splitting process, zCDP is applied to the modified dataset using the specified privacy budgets. This approach ensures that the privacy guarantees are maintained while still allowing for meaningful statistical analysis of the data. Understanding this nuanced approach is crucial for researchers and data users who rely on the CBP dataset for their work. Accurate descriptions of the privacy mechanisms employed are essential for transparency and trust in the data.

The Core Issue: zCDP vs. Per-Record Differential Privacy

The central question revolves around whether describing the CBP dataset as using zCDP with a business establishment as the privacy unit is accurate. The reality is more complex. The dataset employs a per-record differential privacy approach that includes a pre-processing step involving the splitting of records based on predefined thresholds. This pre-processing significantly alters the interpretation of the privacy parameters. Therefore, it's essential to delve deeper into the implications of this splitting mechanism and how it affects the overall privacy guarantees. The splitting process itself introduces a unique dimension to the privacy analysis. By splitting records that exceed certain thresholds, the Census Bureau aims to reduce the sensitivity of the data and enhance privacy protection. However, this also means that the final dataset no longer directly represents the original business establishments. Instead, it consists of a collection of modified records that have been altered to comply with the privacy constraints. This raises questions about the interpretation of the privacy budget and how it relates to the original data. If the data has been split, does the privacy budget still accurately reflect the level of protection afforded to individual business establishments? To address these concerns, it's important to consider alternative ways to characterize the privacy mechanism employed in the CBP dataset. One possibility is to define a new DP flavor that specifically accounts for the splitting process. This would involve developing a formal privacy analysis that takes into account the impact of the splitting on the overall privacy guarantees. Another approach is to describe the CBP dataset as using zCDP with a privacy unit defined as a volume of contribution. This is similar to how the historical pageviews dataset from Wikimedia Foundation is described in the OpenDP Deployments Registry. The key difference is that the splitting mechanism in the CBP dataset is more complex than the simple volume capping used in the Wikimedia dataset. This complexity adds another layer of challenge to accurately characterizing the privacy properties of the CBP data.

Proposed Solutions and Analogies: A New DP Flavor or zCDP with Volume of Contribution?

Considering the intricacies of the CBP's privacy mechanism, two potential solutions arise: defining it as a new DP flavor or describing it as zCDP with a volume of contribution. The former would involve creating a new category in the OpenDP registry to accurately reflect the splitting process unique to the CBP dataset. This would require a detailed explanation of the splitting mechanism and its impact on the overall privacy guarantees. The latter, on the other hand, would involve adapting the existing zCDP framework to account for the complexities of the splitting process. This could be achieved by defining the privacy unit as a volume of contribution, similar to the approach used for the Wikimedia Foundation's historical pageviews dataset. However, it's important to acknowledge that the splitting mechanism in the CBP dataset is more intricate than the simple volume capping used in the Wikimedia dataset. Therefore, a more sophisticated approach would be needed to accurately capture the privacy properties of the CBP data. One way to think about the splitting mechanism is as a form of data transformation that reduces the sensitivity of the data. By splitting records that exceed certain thresholds, the Census Bureau effectively limits the impact of any single record on the final results. This, in turn, reduces the amount of noise that needs to be added to the data to ensure differential privacy. However, it's important to note that the splitting process also introduces a potential source of bias. If the thresholds are not chosen carefully, the splitting process could distort the underlying patterns in the data. Therefore, it's essential to carefully evaluate the impact of the splitting process on the accuracy and reliability of the CBP data. Ultimately, the choice between defining a new DP flavor and describing the CBP data as zCDP with a volume of contribution will depend on the specific goals and priorities of the OpenDP project. If the goal is to provide a highly accurate and detailed description of the privacy mechanism, then defining a new DP flavor may be the best option. However, if the goal is to maintain consistency with the existing OpenDP framework, then describing the CBP data as zCDP with a volume of contribution may be more appropriate.

The Missing Piece: Release of Splitting Thresholds and Their Impact

An important aspect that remains unclear is whether the splitting thresholds used in the CBP dataset were ever officially released. The Census Bureau mentioned a forthcoming paper that was expected to detail these thresholds, but there's no evidence that this paper was ever published. The webinar on differential privacy also doesn't seem to have explicitly revealed these thresholds. Without knowing these thresholds, the raw budget numbers provided for the CBP dataset are incomplete. The thresholds are crucial for understanding the privacy-utility trade-off in the dataset. Knowing these thresholds allows data users to assess the level of privacy protection afforded to the data and to determine whether the data is suitable for their intended use. Without this information, it's difficult to make informed decisions about the use of the CBP dataset. If the splitting thresholds were never released, it raises questions about the transparency of the privacy mechanism used in the CBP dataset. Transparency is a key principle of differential privacy. It's important for data users to understand how the data has been protected and what privacy guarantees have been provided. Without access to the splitting thresholds, it's difficult to fully understand the privacy properties of the CBP data. Furthermore, the lack of publicly available splitting thresholds hinders reproducibility. Researchers who want to replicate the privacy analysis of the CBP dataset cannot do so without knowing the thresholds. This undermines the scientific credibility of the dataset. Therefore, it's essential that the Census Bureau release the splitting thresholds used in the CBP dataset. This would enhance the transparency, usability, and credibility of the dataset. In the meantime, the OpenDP Deployments Registry should clearly state that the splitting thresholds are not publicly available and that this limits the completeness of the privacy parameter description.

Implications of Incomplete Information on Privacy Budgets

Without access to the splitting thresholds, the privacy budgets associated with the CBP dataset become significantly less informative. Understanding the thresholds is crucial for determining the actual level of privacy protection afforded by the DP mechanism. The privacy budget represents the amount of privacy loss that is tolerated when releasing the data. It quantifies the trade-off between privacy and utility. However, the privacy budget only tells part of the story. It doesn't reveal how the privacy loss is distributed across different parts of the data or how the splitting process affects the overall privacy guarantees. To fully understand the privacy implications of the CBP dataset, it's necessary to know the splitting thresholds. These thresholds determine how the data is transformed before the DP mechanism is applied. They also influence the amount of noise that needs to be added to the data to ensure differential privacy. If the thresholds are set too low, the splitting process could distort the data and reduce its utility. If the thresholds are set too high, the data may not be sufficiently protected. Therefore, it's essential to carefully choose the splitting thresholds to balance privacy and utility. The lack of publicly available splitting thresholds makes it difficult for data users to assess the privacy-utility trade-off in the CBP dataset. They cannot determine whether the privacy budget is sufficient to protect the data or whether the splitting process has unduly affected its utility. This limits the ability of data users to make informed decisions about the use of the CBP dataset. Therefore, it's crucial that the Census Bureau release the splitting thresholds used in the CBP dataset. This would enhance the transparency, usability, and credibility of the dataset.

Conclusion: Towards Accurate and Transparent Privacy Descriptions

In conclusion, the current description of the County Business Patterns (CBP) dataset's privacy parameters in the OpenDP Deployments Registry requires revision to accurately reflect the per-record differential privacy approach and the splitting mechanism employed. Whether this is achieved by defining a new DP flavor or by characterizing it as zCDP with a volume of contribution needs further consideration. Crucially, the release of the splitting thresholds is paramount for a complete understanding of the privacy guarantees and the effective use of the CBP data. This discussion highlights the importance of transparency and accuracy in describing privacy mechanisms, ensuring that data users can make informed decisions about data usage. More information about Differential Privacy can be found at OpenDP.org