Secure Tensor Decomposition Using Fully Homomorphic Encryption Scheme

As the rapidly growing volume of data are beyond the capabilities of many computing infrastructures, to securely process them on cloud has become a preferred solution which can both utilize the powerful capabilities provided by cloud and protect data privacy. This paper puts forward a new approach to securely decompose tensor, the mathematical model widely used in data-intensive applications, to a core tensor and some truncated orthogonal bases. The structured, semi-structured as well as unstructured data are all transformed to low-order sub-tensors which are then encrypted using the fully homomorphic encryption scheme. A unified high-order cipher tensor model is constructed by collecting all the cipher sub-tensors and embedding them to a base tensor space. The cipher tensor is decomposed through a proposed secure algorithm, in which the square root operations are eliminated during the Lanczos procedure. The paper makes an analysis of the secure algorithm in terms of time consumption, memory usage and decomposition accuracy. Experimental results reveals that this approach can securely decompose tensor models. With the advancement of fully homomorphic encryption scheme, the proposed secure tensor decomposition method is expected to be widely applied on cloud for privacy-preserving data processing

A tensor-based approach for big data representation and dimensionality reduction

PublishedJournal Article© 2013 IEEE. Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction

Antennae-enriched expression of candidate odorant degrading enzyme genes in the turnip aphid, Lipaphis erysimi

Aphids heavily rely on their olfactory system for foraging behavior. Odorant-degrading enzymes (ODEs) are essential in preserving the olfactory acuity of aphids by removing redundant odorants in the antennae. Certain enzymes within this group stand out as being enriched and/or biased expressed in the antennae, such as carboxylesterases (CXEs), cytochrome P450 (CYPs), glutathione S-transferases (GSTs), and UDP-glycosyltransferases (UGTs). Here, we performed a comparative transcriptome analysis of antennae and body tissue to isolate the antennal ODE genes of turnip aphid Lipaphis erysimi. A dataset of one CXE, seven CYPs, two GSTs, and five UGTs enriched in the antennae was identified and subjected to sequence analysis. Furthermore, qRT-PCR analyses showed that 13 ODE genes (LeCXE6, LeCYP4c1, LeCYP6a2, LeCYP6a13, LeCYP6a14.2, LeCYP6k1, LeCYP18a1, LeGST1, LeUGT1-7, LeUGT2B7, LeUGT2B13, LeUGT2C1.1, and LeUGT2C1.2) were specifically or significantly elevated in antennal tissues. Among these antennae-enriched ODEs, LeCYP4c1, LeCYP6a2, LeCYP6a13, LeCYP6a14.2, LeCYP18a1, LeUGT2B7, and LeUGT2B13 were found to exhibit significantly higher expression levels in alate aphids compared to apterous and nymph aphids, suggesting their putative role in detecting new host plant location. The results presented in this study highlight the identification and expression of ODE genes in L. erysimi, paving the path to investigate their functional role in odorant degradation during the olfactory processes

DNIDS: A Dependable Network Intrusion Detection System Using the CSI-KNN Algorithm

The dependability of an Intrusion Detection System (IDS) relies on two factors: abil-ity to detect intrusions and survivability in hostile environments. Machine learning-based anomaly detection approaches are gaining increasing attention in the network intrusion detection community because of their intrinsic ability to discover novel at-tacks. This ability has become critical since the number of new attacks has kept growing in recent years. However, most of today’s anomaly-based IDSs generate high false positive rates and miss many attacks because of a deficiency in their ability to discriminate attacks from legitimate behaviors. These unreliable results damage the dependability of IDSs. In addition, even if the detection method is sound and effec-tive, the IDS might still be unable to deliver detection service when under attack. With the increasing importance of the IDS, some attackers attempt to disable the IDS before they launch a thorough attack. In this thesis, we propose a Dependable Network Intrusion Detection System (DNIDS) based on the Combined Strangeness and Isolation measure K-Nearest Neigh

FPGA Based Pico-second Time Measurement System for a DIRC-like TOF Detector

A prototype of DIRC-like Time-of-Flight detector (DTOF), including a pico-second time measurement electronics, is developed and tested preliminarily. The basic structure of DTOF is composed of a fused silica radiator connected to fast micro-channel plate PMTs (MCP-PMT), and readout by a dedicated FPGA (Field Programmable Gate Array) based front-end electronics. The full electronics chain consists of a programmable differential amplifier, a dual-threshold differential discriminator, and a timestamp Time-to-Digital convertor. By splitting a MCP-PMT output signal into two identical electronics chains, the coincidence time resolution (CTR) of pure electronics was measured as 5.6 ps. By the beam test in H4 (150GeV/c, Muon) at CERN, the intrinsic CTR of the whole detector prototype reaches 15.0 ps without using time-amplitude correction. The test results demonstrate that the FPGA based front-end electronics could achieve an excellent time performance for TOF detectors. It is very compact, cost effective with a high multi-channel capacity and short measurement dead time, which is very suitable for practical applications of large-scale high performance TOF detectors in particle physics spectrometer

Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns

Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams’ behavioural trends across various modalities, without numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes

Profiles of Sterigmatocystin and Its Metabolites during Traditional Chinese Rice Wine Processing

Mycotoxin pollution is widespread in cereal, which greatly threatens food security and human health. In this study, the migration and transformation of sterigmatocystin (STG) mycotoxin during the contaminated rice wine processing was systematically assessed. QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) coupled with ultrahigh-performance liquid chromatography coupled with tandem mass spectrometry (UPLC−MS/MS) method was firstly established for STG analysis in rice wine. It was found that high levels of rice leaven caused a significant reduction in STG in the fermented rice and wine, which was mainly due to the adsorption of yeast cells and Rhizopus biological degradation. However, compared with rice, the levels of STG in separated fermented wine was significantly decreased by 88.6%, possibly attributed to its high log Kow (3.81) and low water solubility (1.44 mg/L). The metabolites of STG (i.e., monohydroxy STG) were identified in rice wine fermentation for the first time. Moreover, STG disturbed the metabolic profile rice wine composition mainly by glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, purine metabolism pathway, particularly with regard to eight amino acids and sixteen lipids. This study elucidated the STG migration and transformation mechanism during the rice wine processing. The finding provided new analytical method for mycotoxin exposure and pollutant in food production, which may support agricultural production and food security

Development of an Immunoassay for the Detection of Copper Residues in Pork Tissues

The presence of high concentrations of copper (Cu) residues in pork is highly concerning and therefore, this study was designed to develop a high-throughput immunoassay for the detection of such residues in edible pork tissues. The Cu content in the pork samples after digestion with HNO3 and H2O2 was measured using a monoclonal antibody (mAb) against a Cu (II)–ethylenediaminetetraacetic acid (EDTA) complex. The resulting solution was neutralized using NaOH at pH 7 and the free metal ions in the solution were chelated with EDTA for the immunoassay detection. An indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) method was developed for Cu ion analysis. The half maximal inhibitory concentration of the mAb against Cu (II)–EDTA was 5.36 ng/mL, the linear detection range varied between 1.30 and 27.0 ng/mL, the limit of detection (LOD) was 0.43 μg/kg, and the limit of quantification (LOQ) was 1.42 μg/kg. The performances of the immunoassay were evaluated using fortified pig serum, liver, and pork samples and had a recovery rate of 94.53–102.24%. Importantly, the proposed immunoassay was compared with inductively coupled plasma mass spectroscopy (ICP-MS) to measure its performance. The detection correlation coefficients of the three types of samples (serum, pork, and liver) were 0.967, 0.976, and 0.983, respectively. Thirty pork samples and six pig liver samples were collected from local markets and Cu was detected with the proposed ic-ELISA. The Cu content was found to be 37.31~85.36 μg/kg in pork samples and 1.04–1.9 mg/kg in liver samples. Furthermore, we detected the Cu content in pigs with feed supplemented with tribasic copper chloride (TBCC) and copper sulfate (CS) (60, 110, and 210 mg/kg in feed). There was no significant difference in Cu accumulation in pork tissues between the TBCC and CS groups, while a remarkable Cu accumulation was found for the CS group in liver at 210 mg/kg, representing more than a two-fold higher level than seen in the TBCC group. Therefore, the proposed immunoassay was found to be robust and sensitive for the detection of Cu, providing a cost effective and practical tool for its detection in food and other complicated samples