With the rapid development of cloud computing, the privacy security incidents occur frequently, especially data security issues. Cloud users would like to upload their sensitive information to cloud service providers in encrypted form rather than the raw data, and to prevent the misuse of data. The main challenge is to securely process or analyze these encrypted data without disclosing any useful information, and to achieve the rights management efficiently. In this paper, we propose the encrypted data processing protocols for cloud computing by utilizing additively homomorphic encryption and proxy cryptography. For the traditional homomorphic encryption schemes with many limitations, which are not suitable for cloud computing applications. We simulate a cloud computing scenario with flexible access control and extend the original homomorphic cryptosystem to suit our scenario by supporting various arithmetical calculations. We also prove the correctness and security of our protocols, and analyze the advantages and performance by comparing with some latest works.
No abstract
Background: Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death worldwide. Advanced stage CRC, during the recent past, had a dismal prognosis and only a few available treatments. Pumilio homologous protein 1 (PUM1) is reportedly aberrant in human malignancies, including CRC. However, the role of PUM1 in the regulation of tumor-initiating cells (T-ICs) remains unknown. Methods: The levels of messenger RNAs (mRNAs) were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunoblot analyses. Statistical analyses were performed to determine the associations between the levels of PUM1 and tumor features and patient outcomes. Whether PUM1 is a downstream target of miR-218-5p was verified by bioinformatics target gene prediction and qRT-PCR. Results: Herein, it was found that T-ICs, chemoresistance, and recurrent CRC samples all manifest increased PUM1 expression. Functional investigations have shown that PUM1 increased the self-renewal, tumorigenicity, malignant proliferation, and chemoresistance of colorectal cells. PUM1 activates the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) signaling pathway biochemically. Furthermore, it was discovered that miR-218-5p specifically targets T-ICs' PUM1 3'-untranslated region (3'-UTR). More importantly, the PUM1/PI3K/AKT axis regulates CRC cells' responses to treatment with cetuximab, and PUM1 overexpression increased cetuximab resistance. More evidence points to the possibility that low PUM1 may predict cetuximab benefits in CRC patients after analysis of the patient cohort, patient-derived tumor organoids, and patient-derived xenografts (PDXs). Conclusions: Taken together, the result of this work points to the critical function of the miR-218-5p/PUM1/PI3K/AKT regulatory circuit in regulating T-ICs characteristics and thus suggests possible therapeutic targets for CRC.
BACKGROUND Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear. AIM To further elucidate the value of EMT-related genes in LUAD prognosis. METHODS Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model. RESULTS Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group ( P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5). CONCLUSION The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.
As medical data become increasingly important in healthcare, it is crucial to have proper access control mechanisms, ensuring that sensitive data are only accessible to authorized users while maintaining privacy and security. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an attractive access control solution that can offer effective, fine-grained and secure medical data sharing, but it has two major drawbacks: Firstly, decryption is computationally expensive for resource-limited data users, especially when the access policy has many attributes, limiting its use in large-scale data-sharing scenarios. Secondly, existing schemes are based on data users’ attributes, which can potentially reveal sensitive information about the users, especially in healthcare data sharing, where strong privacy and security are essential. To address these issues, we designed an improved CP-ABE scheme that provides efficient and verifiable outsourced access control with fully hidden policy named EVOAC-HP. In this paper, we utilize the attribute bloom filter to achieve policy hiding without revealing user privacy. For the purpose of alleviating the decryption burden for data users, we also adopt the technique of outsourced decryption to outsource the heavy computation overhead to the cloud service provider (CSP) with strong computing and storage capabilities, while the transformed ciphertext results can be verified by the data user. Finally, with rigorous security and reliable performance analysis, we demonstrate that EVOAC-HP is both practical and effective with robust privacy protection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.