A new academic study proposes a way to design privacy mechanisms that can make use of prior knowledge about how data is distributed, even when that information is incomplete. The method allows privacy guarantees to stay mathematically sound while improving how much useful information can be shared.
Researchers from KTH Royal Institute of Technology in Sweden and Inria Saclay in France developed the framework using a measure called pointwise maximal leakage, or PML. Their approach addresses a key challenge in earlier information-theoretic methods, which often assumed perfect knowledge of the data-generating distribution. The new framework shows how to incorporate estimated or partial distribution information to improve data utility, bridging the gap between those methods and tools like local differential privacy, which assume no prior knowledge at all.
Rethinking what privacy depends on
Many privacy frameworks, such as local differential privacy, work under the assumption that system designers have no prior knowledge about the data. The new approach builds on this by showing how even limited knowledge of the data distribution can be used safely to improve utility.
The researchers use a mathematical concept known as an uncertainty set to describe all possible distributions that could have generated the observed data. They then design privacy mechanisms that guarantee a chosen privacy level for every distribution in that set. The privacy measure, PML, helps track how much information an attacker could learn from each possible outcome.
The research explains that traditional local differential privacy methods tend to be conservative because they assume no knowledge about the data. This leads to adding more noise than needed, which harms data utility. The PML approach narrows that gap by making use of whatever knowledge can be safely derived from the data itself.
This design shift resonates with challenges seen in industry. “The paper highlights a real problem we see in practice,” said Onur Alp Soner, CEO of Countly. “Privacy rules are often designed once and left unchanged while the underlying data continues to evolve. In real deployments, each new SDK, feature, or platform shift alters what’s collected. Over time, those changes can quietly invalidate the assumptions that early privacy checks were built on.”
Measuring uncertainty with math
The framework also connects existing results from large-deviation theory with practical privacy guarantees. It uses established bounds that estimate how far an observed sample is likely to be from the true underlying distribution, and then links those bounds to the probability that a privacy guarantee might fail.
This means that systems trained on large datasets can gain strong privacy guarantees with less information loss. The research provides formulas for linking the number of samples, the level of privacy desired, and the probability that the guarantee will fail. The authors found that this failure probability decreases exponentially as more data becomes available.
More data, better privacy
The team applied their method to two well-known privacy mechanisms, the Laplace mechanism and the Gaussian mechanism, which both work by adding noise to data. Using binary data, they compared their PML-based design with traditional local differential privacy methods. The results showed that their approach achieved significantly higher data utility while maintaining similar privacy guarantees.
In one example, the probability of privacy failure was set at one in a billion. Even with that tight condition, the researchers observed that the new method preserved much more of the useful information in the data. As the number of samples used to estimate the data distribution increased, the advantage grew even larger.
Leonhard Grosse, co-author of the research, told Help Net Security that the healthcare sector offers a natural example of where this approach could make a difference. “When dealing with medical data, certain attributes, such as blood type or sex, are known to follow a non-degenerate distribution that is often approximately independent of the sample population,” he explained. “When dealing with data of this form, we therefore expect that practical systems can improve their privacy-utility tradeoff by estimating this distribution and adapting the privacy mechanisms to it.”
Grosse added that while the opportunity is significant, challenges remain for more complex datasets. “One significant challenge in this endeavor will appear in cases with high-dimensional data,” he said. “While distribution estimation in low dimensions comes with good bounds on the estimation error, this behavior disappears in high dimensions. As an example, think of a medical dataset where each entry consists of age, sex, blood type, and DNA sequence. While age, sex, and blood type are low dimensional and usually follow nice distributions, the distribution of DNA sequences is impossibly complex. Hence, simply estimating the joint distribution of the data points is not tractable. For such data, more sophisticated methods will need to be explored.”
Turning theory into design tools
Beyond the case studies, the research provides a set of mathematical results that can be applied to other privacy settings. It shows how to compute optimal mechanisms under uncertainty, including closed-form solutions for simple binary data and a convex optimization program for more complex datasets.
These results mean that privacy engineers could, in theory, design systems that automatically adjust to the available data. The framework explains how to choose privacy parameters to meet a desired balance between protection and accuracy, given a known probability of error.
For organizations hoping to bridge research and practice, Soner suggested practical steps. “Track segment sizes over time. If a group becomes too small, merge or suppress it automatically. Quantify privacy loss as part of monitoring. Treat it like latency or uptime, not just a compliance checkbox,” he said. “Automate enforcement. Build jobs that detect and act on high-risk segments before they reach dashboards or exports.”
He added that while this adds operational work, it also gives companies control. “Privacy becomes measurable and adjustable rather than theoretical, a shift that’s essential for any organization managing sensitive user data at scale,” Soner said. “It’s what will turn frameworks like this from research ideas into practical tools.”
Privacy without the guesswork
This research offers a way to improve one of the biggest tradeoffs in privacy engineering: the loss of utility caused by assuming no prior knowledge about the data-generating process. By allowing systems to safely incorporate limited, empirically derived information, it becomes possible to provide strong privacy guarantees while preserving more data usefulness.
The findings also suggest that privacy guarantees do not have to come at such a steep cost to data utility. The tradeoff can be managed more precisely when uncertainty is built into the design from the start.
The researchers note that the method can be adapted for other kinds of uncertainty, such as when only certain parameters of a model are unknown. They suggest that future work could explore new types of deviation bounds or domain-specific settings, including cases where data follows a known pattern like a normal distribution.
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