Exploiting Record Similarity for Practical Vertical Federated Learning

As the privacy of machine learning has drawn increasing attention, federated learning is introduced to enable collaborative learning without revealing raw data. Notably, \textit{vertical federated learning} (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, existing studies in VFL rarely study the ``record linkage'' process. They either design algorithms assuming the data from different parties have been linked or use simple linkage methods like exact-linkage or top1-linkage. These approaches are unsuitable for many applications, such as the GPS location and noisy titles requiring fuzzy matching. In this paper, we design a novel similarity-based VFL framework, FedSim, which is suitable for more real-world applications and achieves higher performance on traditional VFL tasks. Moreover, we theoretically analyze the privacy risk caused by sharing similarities. Our experiments on three synthetic datasets and five real-world datasets with various similarity metrics show that FedSim consistently outperforms other state-of-the-art baselines

Privacy-Preserving Gradient Boosting Decision Trees

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be further reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines

A new stepwise and piecewise optimization approach for CO2 pipeline

© 2016 . The process of CO2 capture, transportation, enhanced oil recovery (EOR) and storage is one of the best ways for CO2 emission reduction, which is also named as Carbon Capture, Utilization and Storage (CCUS). It has been noted that CO2 transportation cost is an important component of the total investment of CCUS. In this paper, a novel stepwise and piecewise optimization is proposed for CO2 transportation design, which can compute the minimum transportation pipeline levelized cost under the effect of temperature variation. To develop the proposed approach, several models are referred to lay a foundation for the optimization design. The proposed optimal algorithm is validated by using numerical studies, which shows the approach can reduce the levelized cost and improve the optimization performance in comparison with the existing methods

Laboratory Study on Improving Recovery of Ultra-Heavy Oil Using High-Temperature-Resistant Foam

After multiple rounds of steam huff-and-puff processes, an ultra-heavy oil reservoir is prone to excessive steam injection pressure, large heat loss, small sweep range of steam, and steam channeling, thus severely affecting the effective utilization of the oil reservoir. To solve these problems, one-dimensional and three-dimensional (3D) physical simulation tools were used to study the plugging performance of high-temperature composite foams by adding tanning extract and alkali lignin under the influence of some factors such as the reservoir temperature, salinity of formation water, and injection methods. The ultra-heavy oil used in the experiment comes from Shengli Oilfield. Under the condition of surface degassing, the viscosity of ultra-heavy oil could reach 145169 mPa.s at 60 °C. The experimental results show that the foam can produce a synergistic effect with both gel systems, indicating that the gel increases the stability of the foam. The foam can transfer more gel into the high-permeability formation, which can efficiently control the foam. The 3D physical simulation experiments indicated that both the systems enhance the recovery of heavy oil reservoir and reduce its moisture content significantly using steam injection. The method involving tannin extract foam and steam injection increased the recovery by 20% compared to the foam involving only steam injection. The method involving alkali lignin foam and steam injection increased the recovery by 11%

Determinants of 14-3-3σ dimerization and function in drug and radiation resistance

Many proteins exist and function as homodimers. Understanding the detailed mechanism driving the homodimerization is important and will impact future studies targeting the “undruggable” oncogenic protein dimers. In this study, we used 14-3-3σ as a model homodimeric protein and performed a systematic investigation of the potential roles of amino acid residues in the interface for homodimerization. Unlike other members of the conserved 14-3-3 protein family, 14-3-3σ prefers to form a homodimer with two subareas in the dimeric interface that has 180° symmetry. We found that both subareas of the dimeric interface are required to maintain full dimerization activity. Although the interfacial hydrophobic core residues Leu12 and Tyr84 play important roles in 14-3-3σ dimerization, the non-core residue Phe25 appears to be more important in controlling 14-3-3σ dimerization activity. Interestingly, a similar non-core residue (Val81) is less important than Phe25 in contributing to 14-3-3σ dimerization. Furthermore, dissociating dimeric 14-3-3σ into monomers by mutating the Leu12, Phe25, or Tyr84 dimerization residue individually diminished the function of 14-3-3σ in resisting drug-induced apoptosis and in arresting cells at G2/M phase in response to DNA-damaging treatment. Thus, dimerization appears to be required for the function of 14-3-3σ

14-3-3σ Contributes to Radioresistance by Regulating DNA Repair and Cell Cycle via PARP1 and CHK2

14-3-3σ has been implicated in the development of chemo and radiation resistance and in poor prognosis of multiple human cancers. While it has been postulated that 14-3-3σ contributes to these resistances via inhibiting apoptosis and arresting cells in G2–M phase of the cell cycle, the molecular basis of this regulation is currently unknown. In this study, we tested the hypothesis that 14-3-3σ causes resistance to DNA-damaging treatments by enhancing DNA repair in cells arrested in G2–M phase following DNA-damaging treatments. We showed that 14-3-3σ contributed to ionizing radiation (IR) resistance by arresting cancer cells in G2–M phase following IR and by increasing non-homologous end joining (NHEJ) repair of the IR-induced DNA double strand breaks (DSB). The increased NHEJ repair activity was due to 14-3-3σ–mediated upregulation of PARP1 expression that promoted the recruitment of DNA-PKcs to the DNA damage sites for repair of DSBs. On the other hand, the increased G2–M arrest following IR was due to 14-3-3σ–induced Chk2 expression. Implications: These findings reveal an important molecular basis of 14-3-3σ function in cancer cell resistance to chemo/radiation therapy and in poor prognosis of human cancers