Privacy-Preserving and Outsourced Multi-User k-Means Clustering

Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources. As a result, such entities may want to refrain from participating in the PPDM process. To overcome this issue and to take many other benefits of cloud computing, outsourcing PPDM tasks to the cloud environment has recently gained special attention. We consider the scenario where n entities outsource their databases (in encrypted format) to the cloud and ask the cloud to perform the clustering task on their combined data in a privacy-preserving manner. We term such a process as privacy-preserving and outsourced distributed clustering (PPODC). In this paper, we propose a novel and efficient solution to the PPODC problem based on k-means clustering algorithm. The main novelty of our solution lies in avoiding the secure division operations required in computing cluster centers altogether through an efficient transformation technique. Our solution builds the clusters securely in an iterative fashion and returns the final cluster centers to all entities when a pre-determined termination condition holds. The proposed solution protects data confidentiality of all the participating entities under the standard semi-honest model. To the best of our knowledge, ours is the first work to discuss and propose a comprehensive solution to the PPODC problem that incurs negligible cost on the participating entities. We theoretically estimate both the computation and communication costs of the proposed protocol and also demonstrate its practical value through experiments on a real dataset.Comment: 16 pages, 2 figures, 5 table

Transmission line condition prediction based on semi-supervised learning

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take into account the robustness and data demand, this paper proposes a state prediction method based on semi-supervised learning. Firstly, for the expanded feature vector, the regular matrix is used to fill in the missing data, and the sparse coding problem is solved by representation learning. Then, with the help of a small number of labelled samples to initially determine the category centers of line segments in different defective states. Finally, the estimated parameters of the model are corrected using unlabeled samples. Example analysis shows that this method can improve the recognition accuracy and use data more efficiently than the existing models

Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge

The advances in machine learning have revealed its great potential for emerging mobile applications such as face recognition and voice assistant. Models trained via a Neural Network (NN) can offer accurate and efficient inference services for mobile users. Unfortunately, the current deployment of such service encounters privacy concerns. Directly offloading the model to the mobile device violates model privacy of the model owner, while feeding user input to the service compromises user privacy. To address this issue, we propose, tailor, and evaluate Leia, a lightweight cryptographic NN inference system at the edge. Unlike prior cryptographic NN inference systems, Leia is designed with two mobile-friendly perspectives. First, Leia leverages the paradigm of edge computing wherein the inference procedure keeps the model closer to the mobile user to foster low latency service. Specifically, Leia\u27s architecture consists of two non-colluding edge services to obliviously perform NN inference on the encoded user data and model. Second, Leia\u27s realization makes the judicious use of potentially constrained computational and communication resources in edge devices. In particular, Leia adapts the Binarized Neural Network (BNN), a trending flavor of NN model with low memory footprint and computational cost, and purely chooses the lightweight secret sharing techniques to develop secure blocks of BNN. Empirical validation executed on Raspberry Pi confirms the practicality of Leia, showing that Leia can produce a prediction result with 97% accuracy by 4 seconds in the edge environment

Deep Learning-Based Medical Diagnostic Services: A Secure, Lightweight, and Accurate Realization

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising an optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively $413\times$, $19\times$, and $43\times$ bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2\% accuracy loss and could enhance the precision due to the newly introduced activation function family

Phase-controlled asymmetric optomechanical entanglement against optical backscattering

Quantum entanglement plays a key role in both understanding the fundamental aspects of quantum physics and realizing various quantum devices for practical applications. Here we propose how to achieve coherent switch of optomechanical entanglement in an optical whispering-gallery-mode resonator, by tuning the phase difference of the driving lasers. We find that the optomechanical entanglement and the associated two-mode quantum squeezing can be well tuned in a highly asymmetric way, providing an efficient way to protect and enhance quantum entanglement against optical backscattering, in comparison with conventional symmetric devices. Our findings shed a new light on improving the performance of various quantum devices in practical noisy environment, which is crucial in such a wide range of applications as noise-tolerant quantum processing and the backscattering-immune quantum metrology.Comment: To be published in SCIENCE CHINA Physics, Mechanics & Astronom

Screening high potassium efficiency potato genotypes and physiological responses at different potassium levels

Potato (Solanum tuberosum L.) growth and production is highly dependent on potassium (K) levels in the soil. Southwest China is the largest potato production region but it has low availability of soil potassium. To assess the genetic variation in K use efficiency, 20 potato genotypes were collected to compare the yield and K content in a pot experiment. Moreover, ‘Huayu-5’ and ‘Zhongshu-19’ were cultivated in five K applications to investigate the K distribution and sucrose in different organs. The results indicated that there were highly significant effects of K, genotype and K×G interactions on tuber yield, plant and tuber K content, plant K uptake efficiency and K harvest index. Cluster analysis classified 20 potato genotypes into four types: DH (high efficiency at low and high K application), LKH (high efficiency at low K application), HKH (high efficiency at high K application) and DL (low efficiency at low and high K application). The potassium distribution percentage in the tubers of the potassium-efficient genotype was higher than that of the potassium-inefficient genotype under low potassium application. The sucrose content in the tuber gently declined as the application of K rose in both cultivars, and that in the tuber of ‘Huayu-5’ was higher than that in ‘Zhongshu-19’. ‘Huayu-5’ reached the highest yield when the potassium application was 159.45 kg ha-1, and ‘Zhongshu-19’ reached the highest yield when the potassium application was 281.4 kg ha-1. This study indicated that genetic variation for K utilization efficiency existed among 20 genotypes, and yield in low K application and relative yield were suitable criteria for screening K utilization efficiency genotypes

Bis[(1S,1′S)-1,1′-(4-amino-4H-1,2,4-triazole-3,5-di­yl)diethanol-κN 1]bis­(nitrato-κO)zinc

In the title homochiral mononuclear compound, [Zn(NO3)2(C6H12N4O2)2], the ZnII atom is located on a twofold rotation axis and coordinated by two N atoms from two ligands and two O atoms from two NO3 − anions, adopting a distorted tetra­hedral coordination geometry. The compound is enanti­omerically pure and corresponds to the S diastereoisomer, with the optical activity originating from the chiral ligand. In the crystal, mol­ecules are connected into three-dimensional supra­molecular networks through O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds

Paleoclimate evolution of the North Pacific Ocean during the late Quaternary: Progress and challenges

High- and low-latitude climatic processes in the North Pacific Ocean are important components of the global climate system. For example, the interplay among North Pacific atmospheric circulation, ocean circulation, and biological productivity affects atmospheric carbon dioxide levels and marine oxygen concentrations. Here we review recent research on the North Pacific paleoclimatic and paleoceanographic evolution during the late Quaternary and its response to external forcings such as orbital insolation, ice-sheet extent, and greenhouse gas concentrations. First, we summarize the principles and application of relative paleointensity as a critical chronological tool in North Pacific paleoclimate research. Second, we illustrate the latest discoveries on the interaction between North Pacific Intermediate Water formation and high-to-low latitude teleconnection processes. Third, recent progress in linking dust fluxes and marine productivity and their global significance for the carbon cycle are presented. Finally, several key scientific problems are highlighted for future research on ocean-atmosphere-climate interactions in the North Pacific, pointing to the importance of combining paleo-records and modeling simulations. Overall, this review also aims to provide a broad insight into possible future changes of ocean-atmosphere circulation in the North Pacific region under a rapidly warming climate

Dissociate lattice oxygen redox reactions from capacity and voltage drops of battery electrodes.

The oxygen redox (OR) activity is conventionally considered detrimental to the stability and kinetics of batteries. However, OR reactions are often confused by irreversible oxygen oxidation. Here, based on high-efficiency mapping of resonant inelastic x-ray scattering of both the transition metal and oxygen, we distinguish the lattice OR in Na0.6[Li0.2Mn0.8]O2 and compare it with Na2/3[Mg1/3Mn2/3]O2. Both systems display strong lattice OR activities but with distinct electrochemical stability. The comparison shows that the substantial capacity drop in Na0.6[Li0.2Mn0.8]O2 stems from non-lattice oxygen oxidations, and its voltage decay from an increasing Mn redox contribution upon cycling, contrasting those in Na2/3[Mg1/3Mn2/3]O2. We conclude that lattice OR is not the ringleader of the stability issue. Instead, irreversible oxygen oxidation and the changing cationic reactions lead to the capacity and voltage fade. We argue that lattice OR and other oxygen activities should/could be studied and treated separately to achieve viable OR-based electrodes