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Federated learning facilitates collaborative learning among multiple parties while ensuring client privacy. The vulnerability of federated learning to diverse Byzantine attacks stems from the opacity inherent in its local training processes. This issue becomes more pronounced when data among clients lacks independent and identical distribution. Concurrently, the advent of gradient inversion attacks introduces an escalating risk to the confidentiality and integrity of the federated learning system. This work introduces a framework to tackle the difficulties associated with Non-IID data within federated learning. Initially, it classifies diverse data using Euclidean distance to alleviate intra-cluster heterogeneity. A privacy-preserving Byzantine fault tolerance strategy utilizing cosine similarity is implemented within each cluster. We adopt normalizing and introduce historical momentum to the inter-cluster aggregation. The integration of homomorphic encryption ensures that clients' gradient information participates in training under ciphertext, safeguarding against gradient inversion attacks. Lastly, we conduct a comparative analysis against classical and latest algorithms under various conditions. These results validate the effectiveness of our design in enhancing robustness in Non-IID data. [ABSTRACT FROM AUTHOR]

The widespread adoption of deep learning is facilitated in part by the availability of large-scale data for training desirable models. However, these data may involve sensitive personal information, which raises privacy concerns for data providers. Differential privacy has been thought of as a key technique in the privacy preservation field, which has drawn much attention owing to its capability of providing rigorous and provable privacy guarantees for training data. Training deep learning models in a differentially private manner is a topic that is gaining traction as this alleviates the reconstruction and inference of sensitive information effectively. Taking this cue, in this paper, we present here a comprehensive and systematic study on differentially private deep learning from the facets of privacy attack and privacy preservation. We explore a new taxonomy to analyze the privacy attacks faced in deep learning and then survey the type of privacy preservation based on differential privacy to tackle such privacy attacks in deep learning. Finally, we propose the first probe into the real-world application of differentially private deep learning, and then conclude with several potential future research avenues. This survey provides promising directions for protecting sensitive information in training data via differential privacy during deep learning model training. [ABSTRACT FROM AUTHOR]

Trajectory data often provide useful information that can be used in real-life applications, such as traffic management, Geo-marketing, and location-based advertising. However, a trajectory database may contain detailed information about moving objects and associate them with sensitive attributes, such as disease, job, and income. Therefore, improper publishing of the trajectory database can put the privacy of moving objects at risk, especially when an adversary uses partial trajectory information as its background knowledge. The existing approaches for privacy preservation in trajectory data publishing provide the same privacy protection for all moving objects. The consequence is that some moving objects may be offered insufficient privacy protection, while some others may not require high privacy protection. In this paper, we address this problem and present PPTD, a novel approach for preserving privacy in trajectory data publishing based on the concept of personalized privacy. It aims to strike a balance between the conflicting goals of data utility and data privacy in accordance with the privacy requirements of moving objects. To the best of our knowledge, this is the first paper that combines sensitive attribute generalization and trajectory local suppression to achieve a tailored personalized privacy model for trajectory data publishing. Our experiments on two synthetic trajectory datasets suggest that PPTD is effective for preserving personalized privacy in trajectory data publishing. In particular, PPTD can significantly improve the data utility of anonymized trajectory databases when compared with previous work in the literature. [ABSTRACT FROM AUTHOR]

Privacy-preserving record linkage (PPRL) is the process of identifying records in sensitive databases that refer to the same entities in applications where no private or confidential data can be shared by the owners of the databases being linked. In their paper "Blockchain-based Privacy-Preserving Record Linkage — Enhancing Data Privacy in an Untrusted Environment" (Nóbrega et al., 2021) (named BC-PPRL in the following), Nóbrega et al. (2021) proposed the use of blockchain technologies to provide accountability of the parties involved in a PPRL protocol and thereby allow the detection of misbehaving parties. While the use of blockchain techniques is an interesting and novel contribution to the research area of PPRL, as we show in this paper both theoretically and practically using a simple attack method, the BC-PPRL approach has some serious privacy weaknesses. We specifically highlight that one key aspect of the proposed approach, the exchange of Bloom filter segments between the database owners, can reveal substantially more sensitive information compared to what is stated in the paper by Nóbrega et al. (2021). Using a real-world data set we show how our attack can allow a database owner to reidentify with high accuracy a large number of the sensitive values that were encoded in the Bloom filter segments they receive from another database owner. We make the code and data sets of our attack available at: https://github.com/anushkavidanage/bc-pprlSegmentAtomAttack/. [ABSTRACT FROM AUTHOR]