Evidence map›Paper›PMID 40285041›Full record

ReviewSensors (Basel, Switzerland)2025

A Comprehensive Survey of Privacy-Enhancing and Trust-Centric Cloud-Native Security Techniques Against Cyber Threats.

Tuba Arif, Byunghyun Jo, Jong Hyuk Park

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tuba ArifDepartment of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul 01811, Republic of Korea.
Byunghyun JoDepartment of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul 01811, Republic of Korea.
Jong Hyuk ParkDepartment of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), Seoul 01811, Republic of Korea.ORCID 0000-0003-1831-0309

Funding

This research was supported by the Future Challenge Defense Technology Research and Development Project (No. 9150921) hosted by Agency for Defense Development Institute in 2023. 9150921
6 · The paper itself

Abstract

Cloud-native architecture is becoming increasingly popular in today's digital environment, driving the demand for robust security precautions to protect infrastructure and applications. This paper examines a variety of privacy-enhancing and trust-centric tools and techniques intended to meet the unique security requirements within cloud-native environments. Specifically, a variety of solutions are covered, such as runtime protection platforms for real-time threat detection and responses, cloud-native endpoint security solutions for ensuring trust and resilience in dynamic contexts, and service mesh technologies for secure service-to-service communication. Furthermore, we examine the roles of cloud-native encryption, cloud-native identity and access management, and container image scanning technologies in protecting containerized applications and preserving data privacy in transit and at rest. The importance of threat detection and response systems, cloud-native security information and event management (SIEM) solutions, and network security are also covered to strengthen trust and transparency in cloud-native security. We also present a thorough case study that demonstrates how security measures are applied across multiple layers, including application, network, infrastructure, and security, and compliance, to ensure holistic security in a cloud-native architecture. By investigating these privacy-enhancing methods and technologies, organizations may improve the security posture of their cloud-native implementations, reducing risks and ensuring the trustworthiness of their information and applications in the ever-changing ecosystem of today's digital landscape.

Indexed as

AI-driven threat detectioncloud-native securitycyber securityDevSecOpsprivacy-enhancing securitytrust managementzero-trust security

Identifiers

PMID40285041
PMCPMC12030732

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.