In the era of big data, the ability to process and analyze large volumes of data is critical for decision-making, marketing and sales, healthcare and scientific research, etc. However, this capability also brings significant challenges related to data security and privacy. Secure and private query processing is essential to address these challenges. It ensures the protection of private and confidential data, compliance with stringent regulatory requirements, and the maintenance of user trust. Additionally, it enables safe data sharing and collaborative analysis while preserving the utility of the data. By incorporating advanced cryptographic and privacy techniques, secure and private query processing can defend against sophisticated cyber threats. This tutorial highlights the importance of integrating robust security and privacy measures into query processing to build trustworthy database systems. It reviews current systems and protocols that achieve these goals and discusses future directions for easy-to-use query processing under secure and private protection.

Differential privacy (DP) is a leading standard for protecting individual privacy in data collection and analysis. This paper explores the shuffle model of DP, which balances privacy and utility by allowing users to send messages to a trusted shuffler before reaching an untrusted analyzer anonymously. We focus on efficiently implementing the matrix mechanism in shuffle-DP, where efficiency is defined by the number of messages each user sends. Our contributions include a baseline shuffle-DP mechanism that naively adapts the matrix mechanism, followed by an improved mechanism that reduces message complexity while maintaining error levels comparable to central-DP. We demonstrate the versatility of our approach across common query workloads, such as range queries and data cubes, achieving significant improvements in message efficiency. Experimental results confirm that our method outperforms the baseline solution while closely matching the accuracy of central-DP mechanisms.

This demonstration introduces DBPecker, an integrated diagnostic platform tailored for distributed relational database systems. DBPecker leverages a graph-based anomaly modeling approach to capture inter-node dependencies and effectively localize compound anomalies, while a causality-aware metric prioritization module automatically isolates critical performance indicators. By unifying anomaly detection with a comprehensive root cause analysis pipeline, the system facilitates rapid and precise diagnosis in distributed database environments. Evaluated on a multi-node OceanBase cluster, DBPecker not only accelerates the identification of underlying anomalies but also substantially improves operational reliability, offering practical insights and actionable recommendations for real-world distributed database management.

The evaluation of top-k conjunctive queries, a staple in business analysis, often requires evaluating the conjunctive query prior to filtering the top-k results, leading to a significant computational overhead within Database Management Systems (DBMSs). While efficient algorithms have been proposed, their integration into DBMSs remains arduous. We introduce relational algorithms, a paradigm where each algorithmic step is expressed by a relational operator. This allows the algorithm to be represented as a set of SQL queries, enabling easy deployment across different systems that support SQL. We introduce two novel relational algorithms, level-k and product-k, specifically designed for evaluating top-k conjunctive queries and demonstrate that level-k achieves optimal running time for top-k free-connex queries. Furthermore, these algorithms enable easy translation into an oblivious algorithm for secure query evaluations. The presented algorithms are not only theoretically optimal but also exhibit eminent efficiency in practice. The experiment results show significant improvements, with our rewritten SQL outperforming the baseline by up to 6 orders of magnitude. Moreover, our secure implementations not only achieve substantial speedup compared to the baseline with secure guarantees but even surpass those baselines that have no secure guarantees.

The problem of continual observation under differential privacy has been studied extensively in the literature. However, all existing works, with the exception of [28,51], have only studied the simple counting query and its derivatives. Join queries, which are arguably the most important class of queries in relational databases, have only been considered in [28,51], but the solutions offered there have two limitations: First, they only support a few specific graph pattern queries, which are special cases of joins. Second, they require hard degree/frequency constraints on the graph/database instance, and the privatized query answers have errors proportional to these constraints. In this paper, we propose a new differentially private mechanism for continual observation of joins that overcomes these two limitations. Our mechanism supports arbitrary joins and predicates, and do not require any constraints to be given in advance, even over an infinite stream. More importantly, it yields an error that is proportional to the actual maximum degree/frequencies in the graph/database instance at the current time of observation. Such an instance-specific utility guarantee is much preferred for the continual observation problem, where the database size and the query answer may change significantly over time.

We study the problem of random sampling in the secure multi-party computation (MPC) model. In MPC, taking a sample securely must have a cost Ω(n) irrespective to the sample size s. This is in stark contrast with the plaintext setting, where a sample can be taken in O(s) time trivially. Thus, the goal of approximate query processing (AQP) with sublinear costs seems unachievable under MPC. To get around this inherent barrier, in this paper we take a two-stage approach: In the offline stage, we generate a batch of n/s samples with (n) total cost, which can then be consumed to answer queries as they arrive online. Such an approach allows us to achieve an Õ(s) amortized cost per query, similar to the plaintext setting. Based on our secure batch sampling algorithms, we build MASQUE, an MPC-AQP system that achieves sublinear online query costs by running an MPC protocol to evaluate the queries on pre-generated samples. MASQUE achieves the strong security guarantee of the MPC model, i.e., nothing is revealed beyond the query result, which itself can be further protected by (amplified) differential privacy