In recent years, the notion of “the right to be forgotten” (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their requests. Consequently, machine unlearning (MU) has gained considerable attention which allows an ML model to selectively eliminate identifiable information. Evolving from MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within federated learning (FL) settings, which empowers the FL model to unlearn an FL client or identifiable information pertaining to the client. Nevertheless, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges necessitate a tailored design when developing FU algorithms. While various concepts and numerous federated unlearning schemes exist in this field, the unified workflow and tailored design of FU are not yet well understood. Therefore, this comprehensive survey delves into the techniques and methodologies in FU providing an overview of fundamental concepts and principles, evaluating existing federated unlearning algorithms, and reviewing optimizations tailored to federated learning. Additionally, it discusses practical applications and assesses their limitations. Finally, it outlines promising directions for future research.

Neural network (NN) inference services enrich many applications, like image classification, object recognition, facial verification, and more. These NN inference services are increasingly becoming an essential offering from cloud computing providers, where end-users’ data are offloaded to the cloud for inference under a customized model. However, current cloud-based inference services operate on clear inputs and NN models, raising paramount privacy concerns. Individual user data may contain private information that should always remain confidential. Meanwhile, the NN model is deemed proprietary to the model owner as model training requires substantial resources. In this article, we present, tailor, and evaluate Sonic , a lightweight secure NN inference service delegated in the cloud. Sonic leverages the cloud computing paradigm to fully outsource the secure inference, freeing end devices and model owners from being actively online for assistance. Sonic guards both user input and model privacy along the whole service flow. We design a series of secure and efficient NN layer functions purely using lightweight cryptographic primitives. Extensive evaluations demonstrate that Sonic achieves up to $60\times$ bandwidth saving in online inference compared to prior art.

Medical time series data analytics based on dynamic time warping (DTW) greatly benefits modern medical research. Driven by the distributed nature of medical data, the collaboration of multiple healthcare institutions is usually necessary for a sound medical conclusion. Among others, a typical use case is disease screening for public health, where multiple healthcare institutions wish to collaboratively detect over their joint datasets the patients whose medical records have similar features to the given query samples. However, sharing the medical data faces critical privacy obstacles with the increasingly strict legal regulations on data privacy. In this paper, we present the design of a novel system enabling privacy-preserving DTW-based analytics on distributed medical time series datasets. Our system is built from a delicate synergy of techniques from both cryptography and data mining domains, where the key idea is to leverage observations on the advancements in plaintext DTW analytics (e.g., clustering and pruning) to facilitate the scalable computation in the ciphertext domain, through our tailored security design. Extensive experiments over real medical time series datasets demonstrate the promising performance of our system, e.g., our system is able to process a secure DTW query computation over 15K time series sequences in 34 minutes.