Efficient and Privacy Preserving Group Signature for Federated Learning

Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users' device, called clients, and only the training results, called gradients, are sent to the server to be aggregated and generate an updated model. However, we cannot assume that the server can be trusted with private information, such as metadata related to the owner or source of the data. So, hiding the client information from the server helps reduce privacy-related attacks. Therefore, the privacy of the client's identity, along with the privacy of the client's data, is necessary to make such attacks more difficult. This paper proposes an efficient and privacy-preserving protocol for FL based on group signature. A new group signature for federated learning, called GSFL, is designed to not only protect the privacy of the client's data and identity but also significantly reduce the computation and communication costs considering the iterative process of federated learning. We show that GSFL outperforms existing approaches in terms of computation, communication, and signaling costs. Also, we show that the proposed protocol can handle various security attacks in the federated learning environment

Fast and Accurate Dual-Way Streaming PARAFAC2 for Irregular Tensors -- Algorithm and Application

How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular tensor is a collection of matrices whose column lengths are the same while their row lengths are different. In a dual-way streaming setting, both new rows of existing matrices and new matrices arrive over time. PARAFAC2 decomposition is a crucial tool for analyzing irregular tensors. Although real-time analysis is necessary in the dual-way streaming, static PARAFAC2 decomposition methods fail to efficiently work in this setting since they perform PARAFAC2 decomposition for accumulated tensors whenever new data arrive. Existing streaming PARAFAC2 decomposition methods work in a limited setting and fail to handle new rows of matrices efficiently. In this paper, we propose Dash, an efficient and accurate PARAFAC2 decomposition method working in the dual-way streaming setting. When new data are given, Dash efficiently performs PARAFAC2 decomposition by carefully dividing the terms related to old and new data and avoiding naive computations involved with old data. Furthermore, applying a forgetting factor makes Dash follow recent movements. Extensive experiments show that Dash achieves up to 14.0x faster speed than existing PARAFAC2 decomposition methods for newly arrived data. We also provide discoveries for detecting anomalies in real-world datasets, including Subprime Mortgage Crisis and COVID-19.Comment: 12 pages, accept to The 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 202

Role of the pleckstrin homology domain of PLCγ1 in its interaction with the insulin receptor

A thiol-reactive membrane-associated protein (TRAP) binds covalently to the cytoplasmic domain of the human insulin receptor (IR) β-subunit when cells are treated with the homobifunctional cross-linker reagent 1,6-bismaleimidohexane. Here, TRAP was found to be phospholipase C γ1 (PLCγ1) by mass spectrometry analysis. PLCγ1 associated with the IR both in cultured cell lines and in a primary culture of rat hepatocytes. Insulin increased PLCγ1 tyrosine phosphorylation at Tyr-783 and its colocalization with the IR in punctated structures enriched in cortical actin at the dorsal plasma membrane. This association was found to be independent of PLCγ1 Src homology 2 domains, and instead required the pleckstrin homology (PH)–EF-hand domain. Expression of the PH–EF construct blocked endogenous PLCγ1 binding to the IR and inhibited insulin-dependent phosphorylation of mitogen-activated protein kinase (MAPK), but not AKT. Silencing PLCγ1 expression using small interfering RNA markedly reduced insulin-dependent MAPK regulation in HepG2 cells. Conversely, reconstitution of PLCγ1 in PLCγ1−/− fibroblasts improved MAPK activation by insulin. Our results show that PLCγ1 is a thiol-reactive protein whose association with the IR could contribute to the activation of MAPK signaling by insulin