GSK3β Is Involved in JNK2-Mediated β-Catenin Inhibition

We have recently reported that mitogen-activated protein kinase (MAPK) JNK1 downregulates beta-catenin signaling and plays a critical role in regulating intestinal homeostasis and in suppressing tumor formation. This study was designed to determine whether JNK2, another MAPK, has similar and/or different functions in the regulation of beta-catenin signaling.We used an in vitro system with manipulation of JNK2 and beta-catenin expression and found that activated JNK2 increased GSK3beta activity and inhibited beta-catenin expression and transcriptional activity. However, JNK2-mediated downregulation of beta-catenin was blocked by the proteasome inhibitor MG132 and GSK3beta inhibitor lithium chloride. Moreover, targeted mutations at GSK3beta phosphorylation sites (Ser33 and Ser37) of beta-catenin abrogated JNK2-mediated suppression of beta-catenin. In vivo studies further revealed that JNK2 deficiency led to upregulation of beta-catenin and increase of GSK3-beta phosphorylation in JNK2-/- mouse intestinal epithelial cells. Additionally, physical interaction and co-localization among JNK2, beta-catenin and GSK3beta were observed by immunoprecipitation, mammalian two-hybridization assay and confocal microscopy, respectively.In general, our data suggested that JNK2, like JNK1, interacts with and suppresses beta-catenin signaling in vitro and in vivo, in which GSK3beta plays a key role, although previous studies have shown distinct functions of JNK1 and JNK2. Our study also provides a novel insight into the crosstalk between Wnt/beta-catenin and MAPK JNKs signaling

Strongly Unforgeable Certificateless Signature Resisting Attacks from Malicious-But-Passive KGC

In digital signature, strong unforgeability requires that an attacker cannot forge a new signature on any previously signed/new messages, which is attractive in both theory and practice. Recently, a strongly unforgeable certificateless signature (CLS) scheme without random oracles was presented. In this paper, we firstly show that the scheme fails to achieve strong unforgeability by forging a new signature on a previously signed message under its adversarial model. Then, we point out that the scheme is also vulnerable to the malicious-but-passive key generation center (MKGC) attacks. Finally, we propose an improved strongly unforgeable CLS scheme in the standard model. The improved scheme not only meets the requirement of strong unforgeability but also withstands the MKGC attacks. To the best of our knowledge, we are the first to prove a CLS scheme to be strongly unforgeable against the MKGC attacks without using random oracles

CrowdBC: A blockchain-based decentralized framework for crowdsourcing

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester’s task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed crowdsourcing system

GMHL: Generalized Multi-Hop Locks for Privacy-Preserving Payment Channel Networks

Payment channel network (PCN), not only improving the transaction throughput of blockchain but also realizing cross-chain payment, is a very promising solution to blockchain scalability problem. Most existing PCN constructions focus on either atomicity or privacy properties. Moreover, they are built on specific scripting features of the underlying blockchain such as HTLC or are tailored to several signature algorithms like ECDSA and Schnorr. In this work, we devise a Generalized Multi-Hop Locks (GMHL) based on adaptor signature and randomizable puzzle, which supports both atomicity and privacy preserving(unlinkability). We instantiate GMHL with a concrete design that relies on a Guillou-Quisquater-based adaptor signature and a novel designed RSA-based randomizable puzzle. Furthermore, we present a generic PCN construction based on GMHL, and formally prove its security in the universal composability framework. This construction only requires the underlying blockchain to perform signature verification, and thus can be applied to various (non-/Turing-complete) blockchains. Finally, we simulate the proposed GMHL instance and compare with other protocols. The results show that our construction is efficient comparable to other constructions while remaining the good functionalities

Cross-Domain Identity-based Matchmaking Encryption

Recently, Ateniese et al. (CRYPTO 2019) proposed a new cryptographic primitive called matchmaking encryption (ME), which provides fine-grained access control over encrypted data by allowing both the sender and receiver to specify access control policies. The encrypted message can be decrypted correctly if and only if the attributes of the sender and receiver simultaneously meet each other\u27s specified policies. In current ME, when users from different organizations need secret communication, they need to be managed by a single-authority center. However, it is more reasonable if users from different domains obtain secret keys from their own authority centers, respectively. Inspired by this, we extend ME to cross-domain scenarios. Specifically, we introduce the concept of the cross-domain ME and instantiate it in the identity-based setting (i.e., cross-domain identity-based ME). Then, we first formulate and design a cross-domain identity-based ME (IB-ME) scheme and prove its privacy and authenticity in the random oracle model. Further, we extend the cross-domain IB-ME to the multi-receiver setting and give the formal definition, concrete scheme and security proof. Finally, we analyze and implement the schemes, which confirms the efficiency feasibility

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features

A new unpredictability-based RFID privacy model

Ind-privacy and unp-privacy, later refined to unp privacy, are two different classes of privacy models for RFID authentication protocols. These models have captured the major anonymity and untraceability related attacks regarding RFID authentication protocols with privacy, and existing work indicates that unp privacy seems to be a stronger notion when compared with ind-privacy. In this paper, we continue studying the RFID privacy models, and there are two folds regarding our results. First of all, we describe a new traceability attack and show that schemes proven secure in unp privacy may not be secure against this new and practical type of traceability attacks. We then propose a new unpredictability-based privacy model to capture this new type of attacks. Secondly, we show that this new model, where we called it the unp privacy, is stronger than both unp privacy and ind-privacy

Delegating Authentication to Edge: A Decentralized Authentication Architecture for Vehicular Networks

Secure and efficient access authentication is one of the most important security requirements for vehicular networks, but it is difficult to fulfill due to potential security attacks and long authentication delay caused by high vehicle mobility, etc. Most of the existing authentication protocols, either do not consider attacks like single point of failure or do not focus on reducing authentication delay. To address these issues, we introduce an edge-assisted decentralized authentication (EADA) architecture, which provides secure and more communication-efficient authentication by enabling an authentication server to delegate its authentication capability to distributed edge nodes (ENs) such as roadside units (RSUs) and base stations (BSs). Under the architecture, we propose a threshold mutual authentication protocol that supports fast handover, which involves two scenarios, Auth-I and Auth-II. Auth-I only happens once when a vehicle tries to access the network for the first time, while Auth-II happens when a vehicle seamlessly roams between two ENs, i.e., handover. Specifically, for Auth-I, each vehicle can be cooperatively authenticated by t out of n ENs with identity-based signature techniques to obtain an authentication token and the involved ENs can be efficiently authenticated in a batch by the vehicle. For Auth-II, the vehicle can utilize the token as its private credential to achieve fast handover based on identity-based signature without interacting with multiple ENs, which further reduces the authentication delay significantly. In addition, we design a flexible method to support dynamic joining and leaving of ENs without the assistance of a trusted center. We demonstrate that the proposed protocol is secure and efficient through security analysis and performance evaluation