Modern machine learning models towards various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Despite the advances in different privacy technologies, existing methods tend to introduce too much noise, which hampers model accuracy and usefulness. Here, we built a secure and privacy-preserving machine learning (PPML) system by combining federated learning (FL), differential privacy (DP) and shuffling mechanism. We applied this system to analyze data from three sequencing technologies, and addressed the privacy concern in three major tasks of omic data, namely cancer classification with bulk RNA-seq, clustering with single-cell RNA-seq, and the integration of spatial gene expression and tumour morphology with spatial transcriptomics, under three representative deep learning models. We also examined privacy breaches in depth through privacy attack experiments and demonstrated that our PPML-Omics system could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform state-of-the-art systems under the same level of privacy guarantee, demonstrating the versatility of the system in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed model with robust and generalizable empirical performance.
In this letter, a highly sensitive bending sensor based on an embedded multimode D-shaped long period fiber grating (EMD-LPFG) is proposed. The novel sensor is applied to carry out vector bending measurement. The proposed LPFG is fabricated by polishing on the prepared structure which is formed by periodically splicing between single mode fiber (SMF) and multimode fiber (MMF). Since the cross section of the embedded MMF is D-shaped, we named it EMD-LPFG. Due to the asymmetric modulation of the refractive index on the fiber by the CO2 laser, the sensor has the ability to distinguish the bending directions, and the MMFs provide higher bending response. The experimental transmission spectrum can match the simulation results well. The experimental results show that the average bending sensitivities in three orthogonal directions are 70.21 nm/m−1 (0°), 9.75 nm/m−1 (90°), −12.04 nm/m−1 (180°) and 9.98 nm/m−1 (270°), respectively. Meanwhile, the temperature sensitivity is 30 pm/°C in the range of 25 °C to 75 °C. According to the ultra-compact structure with the total length of 2.5 mm, high bending sensitivity and ability to distinguish the bending direction, the novel sensor has potential in bending measurement.
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