Enterprise AI Analysis: SloTEc 2025 - 6th edition of ACM Workshop on Secure IoT, Edge and Cloud systems
Unlocking AI Potential in Secure IoT, Edge and Cloud Systems
In the last years, we have seen an increase in the number of Artificial Intelligence (AI)-powered applications for information retrieval and data science. This fact led to an increasing reliance on distributed computing infrastructures, including Cloud, Edge, and IoT environments. These architectures enable powerful and scalable solutions but also introduce new security and privacy risks that must be addressed at both the system and data levels. Even a single breach on any of the links of the data-service-infrastructure chain may seriously compromise the security of the end-user application. With such a wide attack surface, security must definitely be approached in a holistic way and addressed in any layer where concerns may potentially arise. SIoTEC solicits novel and innovative ideas, proposals, positions and best practices that address the modelling, design, implementation, and enforcement of security in Cloud/Edge/IoT environments. SIoTEC aims at stimulating the submission and discussion of original and innovative contributions for enhancing security management in real-world applications deployed on advanced and distributed computing systems. The workshop will address not only topics related to the secure setup of communications in multi-device and multi-domain environments but also the secure management of data storage and privacy, thus covering a wide range of threats in the development of IoT-oriented services and applications.
Executive Impact & Key Metrics
SIoTEC has been a leading forum for advancing security in distributed systems, consistently bringing together experts and driving innovation in critical domains.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Security Themes
Workshop Impact & Future Directions
Historical Workshop Performance
The workshop focuses on cutting-edge research addressing the most critical security challenges in modern distributed computing. Explore key areas of investigation and innovation.
Blockchain for Secure AI Services
Blockchain technology offers immutable ledgers and decentralized trust, making it ideal for enhancing the security and integrity of AI services deployed across complex IoT, Edge, and Cloud environments. This involves secure data sharing, model provenance, and verifiable AI execution.
Conclusion: Implementing blockchain solutions ensures transparency and tamper-proof operations for critical AI applications, mitigating risks like data manipulation and unauthorized access.
Value Proposition: Enhanced Trust & Immutability
New Attacks
in Distributed AI Systems
Distributed AI systems introduce new vulnerabilities, from data poisoning in federated learning to integrity breaches across edge nodes. Addressing these requires robust, multi-layered security protocols.
Data Security Across Domains and Countries
Ensuring data security and privacy compliance across diverse geographical regions and regulatory frameworks (e.g., GDPR, CCPA) is paramount. Solutions must handle cross-border data transfer, storage, and processing with consistent security policies.
Conclusion: Cross-domain data security requires sophisticated encryption, access control, and legal compliance mechanisms, adaptable to various national and international standards.
Value Proposition: Global Compliance & Protection
Secure Deployment Workflow
Threat Modeling
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Architecture Design
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Secure Coding
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Vulnerability Testing
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Continuous Monitoring
Privacy-Preserving Machine Learning
In multi-device environments, privacy is a major concern. Research explores techniques like homomorphic encryption, federated learning, and differential privacy to enable AI model training and inference without exposing raw sensitive data.
Conclusion: Advancements in privacy-preserving ML enable powerful AI capabilities while safeguarding user privacy, crucial for healthcare and personal data applications.
Value Proposition: Data Utility & Privacy
MITM & Poisoning
Attack Prevention
Protection against Man-in-the-Middle (MITM) and data poisoning attacks is crucial for maintaining the integrity and reliability of AI models and communication channels in IoT/Edge systems.
SIoTEC's objectives extend beyond presenting research; it aims to shape the future of secure AI and distributed systems through collaboration and trend identification.
Highlighting Emerging Research Trends
The workshop provides a platform to identify and discuss novel research trends and open challenges in securing distributed AI systems, guiding future academic and industrial efforts.
Conclusion: By spotlighting cutting-edge areas, SIoTEC helps direct the research community towards the most pressing security problems in AI.
Value Proposition: Future-Proofing AI Security
Promoting Interdisciplinary Discussion
SIoTEC fosters dialogue among experts from cybersecurity, distributed systems, AI, Data Science, and IoT. This cross-pollination of ideas leads to more holistic and innovative security solutions.
Conclusion: Interdisciplinary collaboration is key to addressing the complex, multi-faceted nature of security in advanced computing environments.
Value Proposition: Holistic Security Solutions
Facilitating New Collaborations
The workshop actively creates networking opportunities to encourage the formation of new research collaborations and project ideas, bridging the gap between academic innovation and industrial practice.
Conclusion: By connecting diverse expertise, SIoTEC directly contributes to the development of practical and impactful security technologies.
Value Proposition: Accelerated Innovation Cycles
A look back at the previous editions of the SIoTEC workshop, demonstrating its consistent growth and engagement within the research community.
| Year | Submissions | Accepted Papers | Attendees |
|---|---|---|---|
| 2024 | 17 | 6 full - 2 short | 20 (in person) |
| 2023 | 18 | 9 full - 3 short | 22 (in person) |
| 2022 | 12 | 5 full - 2 short | 15 (hybrid) |
| 2021 | 12 | 4 full - 2 short | 18 (online) |
| 2020 | 17 | 5 full - 3 short | 20 (online) |
Calculate Your Potential ROI with Secure AI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing advanced security protocols in AI-powered distributed systems.
Estimated Annual Savings
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Your Secure AI Implementation Roadmap
A structured approach to integrating cutting-edge security into your distributed AI ecosystems, drawing insights from SIoTEC's research.
Secure Architecture Design
Defining robust security architectures for IoT, Edge, and Cloud environments, integrating zero-trust principles and secure-by-design methodologies from inception.
Threat Intelligence Integration
Incorporating real-time threat intelligence feeds and predictive analytics to proactively identify and mitigate emerging security risks across the distributed infrastructure.
Automated Policy Enforcement
Implementing automated security policy enforcement mechanisms, leveraging AI/ML for continuous compliance checks and adaptive access control.
Post-Quantum Security Assessment
Evaluating and preparing for post-quantum cryptographic standards to ensure long-term data confidentiality against future quantum attacks.
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