In aggregation queries, predicate parameters often reveal user intent. Protecting these parameters is critical for user privacy, regardless of whether the database is public or private. While most existing works focus on private data settings, we address a public data setting where the server has access to the database. Current solutions for this setting either require additional setups (e.g., non-colluding servers, hardware enclaves) or are inefficient for practical workloads. Furthermore, they often do not support range predicates or boolean combinations commonly seen in real-world use cases. To address these limitations, we built HADES, a fully homomorphic encryption (FHE) based private aggregation system for public data that supports point, range predicates, and boolean combinations. Our one-round HADES protocol efficiently generates predicate indicators by leveraging the plaintext form of public data records. It introduces a novel elementwise-mapping operation and an optimized reduction algorithm, achieving latency efficiency within a limited noise budget. Our highly scalable, multi-threaded implementation improves performance over previous one-round FHE solutions by 204x to 6574x on end-to-end TPC-H queries, reducing aggregation time on 1M records from 15 hours to 38 seconds.

The tutorial focuses on Private Information Retrieval (PIR), which allows clients to privately query public or server-owned databases without disclosing their queries. The tutorial covers the basic concepts of PIR such as its types, construction, and critical building blocks, including homomorphic encryption. It also discusses the performance of PIR, existing optimizations for scalability, real-life applications of PIR, and ways to extend its functionalities.

Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private graph secure from an untrusted cloud server, many cryptographic techniques are leveraged to hide the content of the data. However, protecting only the data content is not enough for a graph database. Because the structural information of the graph can be revealed through the database accessing track. In this work, we study the graph neural network (GNN), an important graph workload to mine information from a graph database. We find that the server is able to infer which node is processing during the edge retrieving phase and also learn its neighbor indices during GNN's aggregation phase. This leads to the leakage of the information of graph structure data. In this work, we present SPG, a structure-private graph database with SqueezePIR. Our SPG is built on top of Private Information Retrieval (PIR), which securely hides which nodes/neighbors are accessed. In addition, we propose SqueezePIR, a compression technique to overcome the computation overhead of PIR. Based on our evaluation, our SqueezePIR achieves 11.85× speedup on average with less than 2% accuracy loss when compared to the state-of-the-art FastPIR protocol.

Consider a cloud server that owns a key-value store and provides a private query service to its clients. Preserving client privacy in this setting is difficult because the key-value store ispublic, and a client cannot encrypt or modify it. Therefore, privacy in this context implies hiding the access pattern of a client. Pantheon is a system that cryptographically allows a client to retrieve the value corresponding to a key from apublickey-value store without allowing the server or any adversary to know any information about the key or value accessed. Pantheon devises a single-round retrieval protocol which reduces server-side latency by refining its cryptographic machinery and massively parallelizing the query execution workload. Using these novel techniques, Pantheon achieves a 93X improvement for server-side latency over a state-of-the-art solution.

Geo-distributed databases are the state of the art tools for managing cloud-based data. But maintaining hot records in geo-distributed databases such as Google's Spanner can be expensive, as it synchronizes each update across a majority of replicas. Frequent synchronization poses an obstacle to achieve high throughput for contentious updateheavy workloads. While such synchronizations are inevitable for complex data types, simple data types such as aggregate data can benefit from reduced synchronizations. To this end, we propose an alternate data management system, Samya, to manage aggregate cloud resource usage data. Samya disaggregates available resources and stores fractions of these resources across geo-distributed sites. Dis-aggregation allows sites to serve client requests independently without synchronization for each update. Samya incorporates a learning mechanism to predict future resource demands. If the predicted demand is not satisfied locally, a synchronization protocol, Avantan, is executed to redistribute available resources in the system. Avantan is a novel fault-tolerant consensus protocol where sites agree on the global availability of resources prior to redistribution. Experiments conducted on Google Cloud Platform highlight that dis-aggregating data and reducing synchronizations allows Samya to commit 16x to 18x more transactions than state of the art cloud geo-distributed systems such as Spanner and CockroachDB.