Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP at the record-level within each silo for cross-silo FL. However, a single user's data may extend across multiple silos, and the desired user-level DP guarantee for such a setting remains unknown. In this study, we present Uldp-FL, a novel FL framework designed to guarantee user-level DP in cross-silo FL where a single user's data may belong to multiple silos. Our proposed algorithm directly ensures user-level DP through per-user weighted clipping, departing from group-privacy approaches. We provide a theoretical analysis of the algorithm's privacy and utility. Additionally, we improve the utility of the proposed algorithm with an enhanced weighting strategy based on user record distribution and design a novel private protocol that ensures no additional information is revealed to the silos and the server. Experiments on real-world datasets show substantial improvements in our methods in privacy-utility trade-offs under user-level DP compared to baseline methods. To the best of our knowledge, our work is the first FL framework that effectively provides user-level DP in the general cross-silo FL setting.

Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introduce the Hierarchical and Multi-Resolution Network (HRNet), a novel deep generative model specifically designed to synthesize realistic human mobility data while guaranteeing differential privacy. We first identify the key difficulties inherent in learning human mobility data under differential privacy. In response to these challenges, HRNet integrates three components: a hierarchical location encoding mechanism, multi-task learning across multiple resolutions, and private pre-training. These elements collectively enhance the model's ability under the constraints of differential privacy. Through extensive comparative experiments utilizing a real-world dataset, HRNet demonstrates a marked improvement over existing methods in balancing the utility-privacy trade-off.

Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic attention in recent years. Implementing the TEE on the server side enables each round of FL to proceed without exposing the client's gradient information to untrusted servers. This addresses usability gaps in existing secure aggregation schemes as well as utility gaps in differentially private FL. However, to address the issue using a TEE, the vulnerabilities of server-side TEEs need to be considered---this has not been sufficiently investigated in the context of FL. The main technical contribution of this study is the analysis of the vulnerabilities of TEE in FL and the defense. First, we theoretically analyze the leakage of memory access patterns, revealing the risk of sparsified gradients, which are commonly used in FL to enhance communication efficiency and model accuracy. Second, we devise an inference attack to link memory access patterns to sensitive information in the training dataset. Finally, we propose an oblivious yet efficient aggregation algorithm to prevent memory access pattern leakage. Our experiments on real-world data demonstrate that the proposed method functions efficiently in practical scales.

Differential privacy (DP) is attracting considerable research attention as a privacy definition when publishing statistics of a dataset. This study focused on addressing the limitation that DP inevitably causes two-sided errors. For example, consider a threshold query that asks whether a counting is above a given threshold or not. An answer through the DP mechanism can cause error. This phenomenon is not desirable for sensitive analysis such as the counting of COVID-19-infected individuals (in a dataset) visiting a specific location; misinformation can result in incorrect decision-making which can increase the epidemic. To the best of our knowledge, the problem is yet to be solved. We proposed a variation of DP, namely asymmetric DP (ADP) to solve the problem. ADP can provide reasonable privacy protection and achieve one-sided errors. Finally, experiments were conducted to evaluate the utility of the proposed mechanism for the epidemic analysis using a real-world dataset. The results of study revealed the feasibility of proposed mechanisms.

Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the identities of data anonymous, which subsequently leads to stronger privacy guarantees for systems. However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy. Moreover, implementing a shuffler in a reliable way is not trivial due to known security issues and/or requirements of advanced hardware or secure computation technology.

How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? We study how to construct an explorable privacy-preserving materialized view under differential privacy. No existing state-of-the-art methods simultaneously satisfy the following essential properties in data exploration: workload independence, analytical reliability (i.e., providing error bound for each search query), applicability to high-dimensional data, and space efficiency. To solve the above issues, we propose HDPView, which creates a differentially private materialized view by well-designed recursive bisected partitioning on an original data cube, i.e., count tensor. Our method searches for block partitioning to minimize the error for the counting query, in addition to randomizing the convergence, by choosing the effective cutting points in a differentially private way, resulting in a less noisy and compact view. Furthermore, we ensure formal privacy guarantee and analytical reliability by providing the error bound for arbitrary counting queries on the materialized views. HDPView has the following desirable properties: (a) Workload independence , (b) Analytical reliability , (c) Noise resistance on high-dimensional data , (d) Space efficiency. To demonstrate the above properties and the suitability for data exploration, we conduct extensive experiments with eight types of range counting queries on eight real datasets. HDPView outperforms the state-of-the-art methods in these evaluations.

The COVID-19 pandemic has prompted techno-logical measures to control the spread of the disease. Private contact tracing (PCT) is a promising technique for this purpose. However, the recently proposed Bluetooth-based PCT has several limitations in terms of functionality and flexibility. The existing systems are only able to detect direct contact (i.e., human-human contact) but cannot detect indirect contact (i.e., human-object, such as disease transmission through a surface). Moreover, the rule of risky contact cannot be flexibly changed with the environmental situation and the nature of the virus. In this paper, we propose a secure and efficient trajectory-based PCT system using trusted hardware. We formalize trajectory-based PCT as a generalization of the well-studied private set intersection (PSI), which is mostly based on cryptographic primitives and is thus insufficient. We solve the problem by leveraging trusted hardware such as Intel SGX and designing a novel algorithm to achieve a secure, efficient and flexible PCT system. Our experiments on real-world data show that the proposed system can achieve high performance and scalability. Specifically, our system (one single machine with Intel SGX) can process thousands of queries on 100 million records of trajectory data in a few seconds.