Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques

Solving Small Exponential ECDLP in EC-based Additively Homomorphic Encryption and Applications

Additively Homomorphic Encryption (AHE) has been widely used in various applications, such as federated learning, blockchain, and online auctions. Elliptic Curve (EC) based AHE has the advantages of efficient encryption, homomorphic addition, scalar multiplication algorithms, and short ciphertext length. However, EC-based AHE schemes require solving a small exponential Elliptic Curve Discrete Logarithm Problem (ECDLP) when running the decryption algorithm, i.e., recovering the plaintext $m\in\{0,1\}^\ell$ from $m \ast G$. Therefore, the decryption of EC-based AHE schemes is inefficient when the plaintext length $\ell > 32$. This leads to people being more inclined to use RSA-based AHE schemes rather than EC-based ones. This paper proposes an efficient algorithm called $\mathsf{FastECDLP}$ for solving the small exponential ECDLP at $128$-bit security level. We perform a series of deep optimizations from two points: computation and memory overhead. These optimizations ensure efficient decryption when the plaintext length $\ell$ is as long as possible in practice. Moreover, we also provide a concrete implementation and apply $\mathsf{FastECDLP}$ to some specific applications. Experimental results show that $\mathsf{FastECDLP}$ is far faster than the previous works. For example, the decryption can be done in $0.35$ ms with a single thread when $\ell = 40$, which is about $30$ times faster than that of Paillier. Furthermore, we experiment with $\ell$ from $32$ to $54$, and the existing works generally only consider $\ell \leq 32$. The decryption only requires $1$ second with $16$ threads when $\ell = 54$. In the practical applications, we can speed up model training of existing vertical federated learning frameworks by $4$ to $14$ times. At the same time, the decryption efficiency is accelerated by about $140$ times in a blockchain financial system (ESORICS 2021) with the same memory overhead

Systems analysis of primary Sjögren's syndrome pathogenesis in salivary glands identifies shared pathways in human and a mouse model

Introduction: Primary Sjogren's syndrome (pSS) is a chronic autoimmune disease with complex etiopathogenesis. Despite extensive studies to understand the disease process utilizing human and mouse models, the intersection between these species remains elusive. To address this gap, we utilized a novel systems biology approach to identify disease-related gene modules and signaling pathways that overlap between humans and mice. Methods: Parotid gland tissues were harvested from 24 pSS and 16 non-pSS sicca patients and 25 controls. For mouse studies, salivary glands were harvested from C57BL/6.NOD-Aec1Aec2 mice at various times during development of pSS-like disease. RNA was analyzed with Affymetrix HG U133+2.0 arrays for human samples and with MOE430+2.0 arrays for mouse samples. The images were processed with Affymetrix software. Weighted-gene co-expression network analysis was used to identify disease-related and functional pathways. Results: Nineteen co-expression modules were identified in human parotid tissue, of which four were significantly upregulated and three were downregulated in pSS patients compared with non-pSS sicca patients and controls. Notably, one of the human disease-related modules was highly preserved in the mouse model, and was enriched with genes involved in immune and inflammatory responses. Further comparison between these two species led to the identification of genes associated with leukocyte recruitment and germinal center formation. Conclusion: Our systems biology analysis of genome-wide expression data from salivary gland tissue of pSS patients and from a pSS mouse model identified common dysregulated biological pathways and molecular targets underlying critical molecular alterations in pSS pathogenesis

Is human blood a good surrogate for brain tissue in transcriptional studies?

Abstract Background Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). Results We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10-90); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. Conclusions Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable

Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression

Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) frequency bands. The frontopolar region contained the greatest number of “hub nodes” (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD

Gene expression deconvolution and co-expression methods

Gene expression analysis provides the link between genome information and phenotype, and is widely used in biomedical research. With the rapid advance of high-throughput technology, it is feasible to measure global mRNA expression in multiple samples at low cost. Over the past decade, many computational and statistical methods have been developed to interpret large-scale gene expression data. However, two questions still have not been thoroughly investigated: 1) how to study gene expression preservation across different tissues, like between brain and blood; and 2) how to analyze the gene expression data generated from heterogeneous tissues comprised of many cell types? Blood samples are an important surrogate to study neurological diseases due to the limited access of brain samples. My dissertation first investigated the gene expression preservation between brain and blood by cross-referencing three brain expression data sets (from cortex, cerebellum and caudate nucleus) with two large blood data sets. While previous studies have focused on the preservation of individual gene expression levels across the two tissues, I utilized a systems biology approach to study the preservation of gene co-expression modules. Since oligodendrocytes, astrocytes, and neurons are not present in blood, it is not surprised that only a handful of human brain modules showed evidence of preservation in human blood while global transcriptome organization is poorly preserved. These shared relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable. For the second question, several previous publications have proposed gene expression deconvolution methods, including estimating cell abundances or cell type-specific gene expression (CTSE) values, for admixed samples comprised of distinct cell types. These methods have not yet been widely adopted since comprehensive empirical evaluations are needed to assess their reliability. Here I evaluated different types of expression deconvolution methods in four empirical data sets, including a neuro-scientific application. Since cell type-specific estimation of the mean value for individual genes is sometimes problematic, we propose to consider sets of genes (as opposed to individual genes) and show that this can increase the accuracy of CTSE estimation. Furthermore, comprehensive simulation studies are used to evaluate the effect of mis-specifying cell types. Our simulations indicated that erroneously omitting cell types from the analysis only has an adverse effect on CTSE estimation if the omitted cell type has a high abundance. We also present two R functions, proportionsInAdmixture and populationMeansInAdmixture, which implement cell abundance estimation and CTSE estimation, respectively

3-{1-[2-(2-Chlorophenyl)hydrazinylidene]-2,2,2-trifluoroethyl}-7-diethylamino-2H-chromen-2-one

The title compound, C21H19ClF3N3O2, has a structure related to other coumarin derivatives that have been used as fluorescent probes of metal ions. The dihedral angle between the coumarin ring system and the chlorobenzene ring is 42.99 (9)°. Intramolecular hydrogen bonding occurs via N—H...O and N—H...Cl interactions, generating S(7) and S(5) rings, respectively. The crystal packing is stabilized by weak C—H...O hydrogen bonds

Synthesis of Porous Fe/C Bio-Char Adsorbent for Rhodamine B from Waste Wood: Characterization, Kinetics and Thermodynamics

In the past decades, dyes waste waters produced from industries have become a major source of environmental pollution causing the destruction of aquatic communities in the ecosystem and greatly threatened human health. Herein, a novel magnetic adsorbent was synthesized by carbonizing iron (III) 2,4-pentanedionate (Fe(acac)3) pre-enriched forestry waste wood at a pyrolysis temperature of 1000 °C. The characterization of the adsorbent conducted via SEM, EDS, VSM, XRD, XPS, and FT-IR spectroscopy. The adsorption trend followed the pseudo-second order kinetics model. The corresponding adsorption performance was efficient with an equilibrium time of only 1 min. Affect factors on the adsorption performance, such as adsorbent dosage, contact time and temperature, were investigated. The magnetic bio-char showed a high adsorption capacity and an efficient adsorption toward RhB, implying great potential application in the treatment of colored wastewaters

Hydroxychloroquine ameliorates immune functionality and intestinal flora disorders of IgA nephropathy by inhibition of C1GALT1/Cosmc pathway

Hydroxychloroquine (HCQ) has emerged as a potential and secure antiproteinuric agent in IgA nephropathy (IgAN). This study endeavored to explore the impact of HCQ on the immune functionality and intestinal flora disorders in IgAN rats, as well as to elucidate the underlying mechanisms through in vivo and in vitro experiments. IgAN model was established in Sprague-Dawley rats through the administration of BSA, LPS, and CCl4, and the IgAN rats received a continuous 8-week treatment with HCQ. Moreover, the human glomerular mesangial cells (HMCs) were incubated with IgA1 to establish an in vitro cellular model of IgAN. At the end of experimental period, samples were collected for further analysis. HCQ ameliorated the elevated levels of 24hUTP, SCr, BUN, the number of urinary RBC, and the activation of inflammation-related proteins within the TLR4/NF-κB signaling pathway. In the IgAN rat group, there was a pronounced escalation in IgA deposition, mesangial matrix hyperplasia, and glomerular inflammatory cell infiltration, while the administration of HCQ effectively mitigated these pathological changes. In addition, the reduced production of CD4+CD25+Foxp3+ Treg in the IgAN group was effectively reversed by HCQ. Furthermore, HCQ has the capacity to restore the compromised state of the intestinal mucosal barrier induced by IgAN and mitigate the circumstances of intestinal permeability and disruption in the intestinal flora. HCQ diminishes IgA aberrant glycosylation levels, ameliorates renal and intestinal histopathological damage, and attenuates intestinal flora disorders and immune dysfunction in IgAN rats by means of activating the C1GALT1/Cosmc pathway.</p

A Review of Manganese-Oxidizing Bacteria (MnOB): Applications, Future Concerns, and Challenges

Groundwater serving as a drinking water resource usually contains manganese ions (Mn2+) that exceed drinking standards. Based on the Mn biogeochemical cycle at the hydrosphere scale, bioprocesses consisting of aeration, biofiltration, and disinfection are well known as a cost-effective and environmentally friendly ecotechnology for removing Mn2+. The design of aeration and biofiltration units, which are critical components, is significantly influenced by coexisting iron and ammonia in groundwater; however, there is no unified standard for optimizing bioprocess operation. In addition to the groundwater purification, it was also found that manganese-oxidizing bacteria (MnOB)-derived biogenic Mn oxides (bioMnOx), a by-product, have a low crystallinity and a relatively high specific surface area; the MnOB supplied with Mn2+ can be developed for contaminated water remediation. As a result, according to previous studies, this paper summarized and provided operational suggestions for the removal of Mn2+ from groundwater. This review also anticipated challenges and future concerns, as well as opportunities for bioMnOx applications. These could improve our understanding of the MnOB group and its practical applications