Differential Privacy (DP) is a widely adopted standard for privacy-preserving data analysis, but it assumes a uniform privacy budget across all records, limiting its applicability when privacy requirements vary with data values. Per-record Differential Privacy (PrDP) addresses this by defining the privacy budget as a function of each record, offering better alignment with real-world needs. However, the dependency between the privacy budget and the data value introduces challenges in protecting the budget's privacy itself. Existing solutions either handle specific privacy functions or adopt relaxed PrDP definitions. A simple workaround is to use the global minimum of the privacy function, but this severely degrades utility, as the minimum is often set extremely low to account for rare records with high privacy needs. In this work, we propose a general and practical framework that enables any standard DP mechanism to support PrDP, with error depending only on the minimal privacy requirement among records actually present in the dataset. Since directly revealing this minimum may leak information, we introduce a core technique called privacy-specified domain partitioning , which ensures accurate estimation without compromising privacy. We also extend our framework to the local DP setting via a novel technique, privacy-specified query augmentation . Using our framework, we present the first PrDP solutions for fundamental tasks such as count, sum, and maximum estimation. Experimental results show that our mechanisms achieve high utility and significantly outperform existing Personalized DP (PDP) methods, which can be viewed as a special case of PrDP with relaxed privacy protection.

In the standard model of differential privacy (DP), every user's privacy is treated equally, which is captured by a single privacy parameter \varepsilon. However, in many real-world situations, users may have diverse privacy concerns and requirements, some conservative while others liberal. This is formalized by the model of personalized differential privacy (PDP), where each user may have a different privacy parameter \varepsilon. However, existing techniques for PDP cannot provide good utility for many fundamental problems such as basic counting and sum estimation. In this paper, we present the personalized truncation mechanism for these problems under PDP. We first show that, theoretically, it is never worse than previous mechanisms (up to polylogarithmic factors) on any instance, while can be much better in certain cases. Then we use extensive experiments on both real and synthetic data to demonstrate its empirical advantages. Our mechanism also works for user-level DP, thus supporting a large class of SJA queries over relational databases under foreign-key constraints.

Differential privacy (DP) has garnered significant attention from both academia and industry due to its potential in offering robust privacy protection for individual data during analysis. With the increasing volume of sensitive information being collected by organizations and analyzed through SQL queries, the development of a general-purpose query engine that is capable of supporting a broad range of SQLs while maintaining DP has become the holy grail in privacy-preserving query release. In this demonstration, we present DOP-SQL, a DP SQL system that can answer a broad class of queries consisting of the selection, projection, aggregation, join, and group by operators. DOP-SQL has integrated a suite of down-neighborhood optimal DP mechanisms, thus achieving state-of-the-art utility. The current implementation of DOP-SQL is based on PostgreSQL, but its extensible feature allows it to be used in conjunction with any standard SQL engine.

Answering relational queries under differential privacy has attracted a lot of attention in recent years due to growing concerns on personal privacy, and instance-optimal mechanisms have been developed for a single query. However, most real-world data analytical tasks require multiple queries to be answered under a total privacy budget. The standard solution to extend the single-query mechanism to multiple queries is via privacy composition. However, we observe that this may yield an error bound that could be a d0.5-factor worse from the optimal, where d is the number of queries. In this paper, we present a different, more holistic approach that closes this gap. In addition to theoretical optimality, our new mechanism also significantly outperforms privacy composition in practice, especially on more skewed data and large d.

Whenever randomness is involved in query processing, confidence intervals are commonly returned to the user to indicate the statistical significance of the query answer. However, this problem has not been explicitly addressed under differential privacy, which must use randomness by definition. For some classical mechanisms whose noise distribution does not depend on the input, such as the Laplace and the Gaussian mechanism, deriving confidence intervals is easy. But the problem becomes nontrivial for queries whose global sensitivity is large or unbounded, for which these classical mechanisms cannot be applied. There are three main techniques in the literature for dealing with such queries: the exponential mechanism, the sparse vector technique, and the smooth sensitivity. In this paper, for each of the three techniques we design mechanisms to produce confidence intervals that are (1) differentially private; (2) correct, i.e., the interval contains the true query answer with the specified confidence level; and (3) have a utility guarantee matching that of the original mechanism, up to constant factors. Then we show how to apply our techniques to a variety of problems ranging from simple statistics (e.g., mean, median, maximum) to graph pattern counting and conjunctive queries.