Bibliography

Sources

The bibliography: 182 public sources, 171 of them cited by at least one record. Tier A is peer-reviewed work, standards and government publications; tier B is preprints, reports, documentation and code; tier C is expert blogs and talks. See the citation policy.

Seeded from Will Hodgkins' AI Workload Verification Papers bibliography (CC BY 4.0), which builds on earlier reading lists by Mauricio Baker and James Petrie.

TitleYearTierTypeCited by
A primer on secure enclaves
Tinfoil · Tinfoil documentation
2026BDocumentation5
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
Z. Peng et al. · Artificial Intelligence Review, vol. 59, no. 7, article 157
2026APeer-reviewed2
A System Overview for Near-Term, Low-Trust AI Compute Verification
N. Cankaya · Machine Intelligence Research Institute
2026BTechnical report38
About: Oxford Martin AIGI
· Oxford Martin AI Governance Initiative
2026BDocumentation1
Adversarial Entropy Inflation Against Gumbel-Based Inference Verification
N. Kezins · arXiv
2026BPreprint7
AI 2040 Plan A — Verification SITREP
Amodo Design · Amodo Design
2026CBlog / article7
Amodo-Design/Inference-Recomputation-Prototype (GitHub repository)
Amodo Design · GitHub
2026BCode3
Amodo-Design/PoSE-Memory-Wiping (GitHub repository)
Amodo Design · GitHub
2026BCode2
Auditor-in-a-Box: Tools for Third-Party Auditing
R. Rinberg & B. Penchas · LessWrong
2026CBlog / article3
Backend infrastructure
Tinfoil · Tinfoil documentation
2026BDocumentation4
Batch Invariance (vLLM documentation)
vLLM project · vLLM documentation (GitHub, docs/features/batch_invariance.md)
2026BDocumentation1
Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing
J. De Meulemeester et al. · 47th IEEE Symposium on Security and Privacy (S&P 2026)
2026APeer-reviewed6
Bit-Exact AI Inference Verification Without Performance Tradeoffs
N. Cankaya · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint8
Components of a Frontier AI Slowdown
A. Chan · A Strange Attractor
2026CBlog / article
Cove: Compositional Multi-Party Confidential Workflows for Verifiable AI Governance
S. Ding et al. · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint1
Cove: Compositional Multi-Party Confidential Workflows for Verifiable AI Governance (reference implementation)
covehub · GitHub
2026BCode1
Covert AI Projects
B. Halstead & T. Larsen · AI 2040
2026CBlog / article4
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI · arXiv
2026BTechnical report1
Detecting Compute Structuring in AI Governance Is Likely Feasible
E. Seferis & T. Fist · Proceedings of the AAAI Conference on Artificial Intelligence 40(44), pp. 37904–37912 (AAAI-26, Special Track on AI Alignment)
2026APeer-reviewed
Detecting Hidden ML Training With Zero-Overhead Telemetry
R. Rahman & S. Tajdari · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint5
Does Distributed Training Undermine Compute Governance?
R. Rahman · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint1
Enabling Verifiably-Scoped Monitoring through Large Language Models and Trusted Compute
B. Penchas et al. · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint3
Example Schemes for Verifying High-Stakes AI Agreements
Amodo Design · Amodo Design
2026CBlog / article9
Experiments: Lucid Labs
· Lucid Computing
2026BDocumentation2
Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors
N. Cankaya et al. · arXiv
2026BPreprint3
Fitting a Network TAP to our Inference Verification Prototype
Amodo Design · Amodo Design
2026CBlog / article2
From Verifiability to Model-Weight Security
Attestable · Attestable blog
2026CBlog / article3
Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
M. Brundage et al. · arXiv
2026BPreprint2
Get Involved in Verification
AI Futures Project · AI 2040
2026CBlog / article6
GPU Fingerprinting for Location Verification
W. Tee & J. Happel · arXiv
2026BPreprint2
Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains
R. Rinberg et al. · arXiv
2026BPreprint4
Hardware AI Governance Lab
· Oxford Martin AI Governance Initiative
2026BDocumentation2
Hardware Mechanisms to Dynamically Throttle AI Performance
H. Ma et al. · arXiv
2026BPreprint1
Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
S. Ansari · arXiv
2026BPreprint5
Hawkeye: Reproducing GPU-Level Non-Determinism
E. Badash et al. · Proceedings of Machine Learning and Systems 8 (MLSys 2026)
2026APeer-reviewed2
Highly Secure Inference Data Centers: A Vertically Integrated Strategy for Security Engineering
S. F. Comer et al. · RAND Corporation (Research Report RR-A4827-1)
2026BTechnical report4
Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference
C. Gong et al. · arXiv
2026BPreprint2
How Tinfoil Proves Exactly What Model Is Running
Tinfoil Team · Tinfoil
2026CBlog / article5
How verification works in Tinfoil
Tinfoil · Tinfoil documentation
2026BDocumentation4
Improving Disk Wiping Speed for Memory Wipes
Amodo Design · Amodo Design
2026CBlog / article2
inference-verification: Inference Verification Prototype
Singapore AI Safety Hub (SASH) · GitHub
2026BCode3
Intelligence Security Laboratories: Building secure infrastructure for transformative AI
· Intelligence Security Laboratories
2026BDocumentation2
Internationalising AI Verification
Singapore AI Safety Hub (SASH) · SASH blog
2026CBlog / article5
Kraken: Higher-order EM Side-Channel Attacks on DNNs in Near and Far Field
P. Horvath et al. · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2026)
2026APeer-reviewed1
LLM-42: Enabling Determinism in LLM Inference with Verified Speculation
R. Gond et al. · arXiv
2026BPreprint1
Lucid Computing: Verifiable AI. Proven in hardware.
· Lucid Computing
2026BDocumentation3
Lucid Developer Platform documentation
· Lucid Computing
2026BDocumentation2
Lucid Labs: the verification flywheel
· Lucid Computing
2026BDocumentation2
Memory Wipes - Performance Analysis
Amodo Design · Amodo Design
2026CBlog / article3
modelwrap: Reproducible dm-verity read-only image of Huggingface models
Tinfoil · GitHub
2026BCode4
NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs
Z. Wang · International Conference on Information and Communications Security (ICICS 2026)
2026APeer-reviewed1
Near-Term Verification Methods for AI Chip Exports
B. Avellar & E. Grunewald · arXiv
2026BPreprint4
Network Tapping for AI Verification: A Technical Assessment
Amodo Design · Amodo Design
2026CBlog / article2
Network Taps — A First Test
Amodo Design · Amodo Design
2026CBlog / article2
Network Traffic Hashing
Amodo Design · Amodo Design
2026CBlog / article2
NIST Computer Security Resource Center (CSRC) Glossary
National Institute of Standards and Technology · NIST Computer Security Resource Center
2026AGovernment document8
On restraining AI development for the sake of safety
J. Carlsmith · Joseph Carlsmith
2026CBlog / article
On TEEs for Privacy-Preserving Monitoring in AI Governance
Gloria Z · MIRI Technical Governance Team
2026CBlog / article12
Our Team: Intelligence Security Laboratories
· Intelligence Security Laboratories
2026BDocumentation2
Pacing AI Requires Proof
Attestable · Attestable blog
2026CBlog / article8
PAL*M: Property Attestation for Large Generative Models
P. Chantasantitam et al. · arXiv
2026BPreprint7
Pearl Floating Point Scheme Specification
Pearl Research Team · Pearl Research Labs
2026BTechnical report4
Pearl INT Whitepaper
Pearl Research Labs · Pearl Research Labs
2026BTechnical report3
pearl: Monorepo for the Pearl network
Pearl Research Labs · GitHub
2026BCode3
Planet Reports Financial Results for Second Quarter of Fiscal Year 2027
Planet Labs PBC · Business Wire (press release)
2026CBlog / article1
Privacy-Preserving AI Verification via Minimal Information Disclosure
S. Abdelghafar & G. Kulp · arXiv
2026BPreprint1
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
X. Jin et al. · arXiv
2026BPreprint2
Proving LLMs at Scale
Attestable · Attestable blog
2026CBlog / article3
Scaling Recomputation Inference Verification
Amodo Design · Amodo Design
2026CBlog / article4
Sovereignty Certificates Working Group
· sovcert.org
2026BDocumentation1
Suppressing Side Channels in an Untrusted Data Center via Retrofitted Defenses
N. Cankaya · MIRI Technical Governance Team
2026CBlog / article7
TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition
J. Chuang et al. · 2026 IEEE Symposium on Security and Privacy (SP)
2026APeer-reviewed6
The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use
N. Cankaya · The Datacenter Lie Detector
2026CBlog / article6
The Tray as a Bandwidth Boundary
Amodo Design · Amodo Design
2026CBlog / article3
The Usefulness Gap in Proof-of-Useful-Work: An Empirical Study of Pearl's cuPOW Protocol
A. Basu · arXiv
2026BPreprint2
Timing and Memory Telemetry on GPUs for AI Governance
S. K. Monfared et al. · arXiv
2026BPreprint4
Tracking Hyperscale AI Data Center Growth with Satellite Imagery
C. Krawec · Federation of American Scientists
2026BTechnical report4
Traffic Shaping for Workload Classification
Lucid Computing · Lucid Computing (Substack)
2026CBlog / article3
Understanding Data Center Power Delivery
Amodo Design · Amodo Design
2026CBlog / article2
Verifiable constraints on frontier training via proofs of compartmentalization
D. Reuter et al. · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint
Verifiable Semiconductor Manufacturing
A. Ilhan et al. · Oxford Martin AI Governance Initiative
2026BTechnical report1
Verifiable-ClawGuard: proof-of-guardrail reference code
SaharaLabsAI · GitHub
2026BCode1
Verification Plan
R. Dean · AI 2040
2026CBlog / article15
Verifying AI Compute by Bounding Unexplained Information Exfiltration
J. Petrie & Y. Mühlhäuser · ICML 2026 Workshop on Technical AI Governance Research
2026BPreprint1
Verifying international AI deals: Plan A, the state-of-play, and what you can do to help
T. Milton et al. · Amodo (Substack)
2026CBlog / article3
Workload Identification with Physical Side Channels for AI Governance
S. Gargiulo & G. Kulp · arXiv
2026BPreprint1
Zero knowledge verification for frontier AI training is possible
P. Peigné et al. · arXiv
2026BPreprint4
adamkarvonen/difr (GitHub repository)
A. Karvonen · GitHub
2025BCode2
AI Security RFDs
AI Security Forum · AI Security Forum
2025CForum / discussion
An International Agreement to Prevent the Premature Creation of Artificial Superintelligence
A. Scher et al. · Machine Intelligence Research Institute
2025BTechnical report7
Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
C. Schnabl et al. · ICML 2025 Workshop on Technical AI Governance
2025BPreprint9
BarraCUDA: Edge GPUs do Leak DNN Weights
P. Horvath et al. · 34th USENIX Security Symposium
2025APeer-reviewed2
Boost GPU Memory Performance with No Code Changes Using NVIDIA CUDA MPS
S. Nassernia · NVIDIA Technical Blog
2025BBlog / article1
Defeating Nondeterminism in LLM Inference
H. He & Thinking Machines Lab · Thinking Machines Lab: Connectionism
2025CBlog / article3
Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry
Z. Chen et al. · arXiv
2025BPreprint1
DiFR: Inference Verification Despite Nondeterminism
A. Karvonen et al. · arXiv
2025BPreprint8
Executive Order 14148: Initial Rescissions of Harmful Executive Orders and Actions
Executive Office of the President · Federal Register, 90 FR 8237 (document 2025-01901, published 2025-01-28)
2025AGovernment document2
Faster AI Diffusion Through Hardware-Based Verification
N. Ammann & D. Dalrymple · Institute for Progress
2025CBlog / article
Flexible Hardware-Enabled Guarantees for AI Compute
J. Petrie et al. · arXiv
2025BPreprint7
Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads
J. Petrie · ICML 2025 Workshop on Technical AI Governance
2025BPreprint1
GuardAIn: Protecting Emerging Generative AI Workloads on Heterogeneous NPU
A. Dhar et al. · 2025 IEEE Symposium on Security and Privacy
2025APeer-reviewed1
Hardware-Enabled Mechanisms for Verifying Responsible AI Development
A. O'Gara et al. · arXiv
2025BPreprint5
Has My System Prompt Been Used? Large Language Model Prompt Membership Inference
R. Levin et al. · arXiv
2025BPreprint1
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning
Prime Intellect Team et al. · arXiv
2025BTechnical report3
International AI Safety Report
Y. Bengio et al. · International AI Safety Report
2025BTechnical report
International Security Applications of Flexible Hardware-Enabled Guarantees
O. Aarne & J. Petrie · arXiv
2025BPreprint1
Introducing the Frontier Data Centers Hub
Epoch AI · Epoch AI
2025CBlog / article3
Location Verification for AI Chips (issue brief)
A. Brass & O. Aarne · Institute for AI Policy and Strategy
2025BTechnical report3
Mechanisms to Verify International Agreements About AI Development
A. Scher & L. Thiergart · arXiv
2025BPreprint18
MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs
R. Ding et al. · 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS 2025)
2025APeer-reviewed
NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper)
NVIDIA · NVIDIA documentation
2025BDocumentation4
Open Problems in Technical AI Governance
A. Reuel et al. · Transactions on Machine Learning Research
2025APeer-reviewed2
Opt-In NVIDIA Software Enables Data Center Fleet Management
NVIDIA · NVIDIA Blog
2025BBlog / article2
PrimeIntellect-ai/toploc (GitHub repository)
Prime Intellect · GitHub
2025BCode3
Proofs of Useful Work from Arbitrary Matrix Multiplication
I. Komargodski et al. · arXiv
2025BPreprint3
RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP
B. Schlüter & S. Shinde · 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS '25)
2025APeer-reviewed6
SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020)
AMD · AMD product security bulletin
2025BDocumentation5
Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System
I. Latif et al. · IEEE Access, vol. 13, pp. 61740–61747
2025APeer-reviewed1
Sovereignty Certificates: draft specification, version 0.1.0
Sovereignty Certificates Working Group · GitHub (Lucid-Computing/sovereignty-certificate-specification)
2025BDocumentation4
SYNTHETIC-2
Prime Intellect · Prime Intellect blog
2025CBlog / article2
Technical Options for Flexible Hardware-Enabled Guarantees
J. Petrie & O. Aarne · arXiv
2025BPreprint3
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
J. M. Ong et al. · Proceedings of the 42nd International Conference on Machine Learning (PMLR 267), pp. 47196-47211
2025APeer-reviewed3
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference (blog post)
Prime Intellect · Prime Intellect blog
2025CBlog / article2
Towards Deterministic Inference in SGLang and Reproducible RL Training
The SGLang Team · LMSYS Org blog
2025CBlog / article1
TSMC most definitely has a golden record of all AI chips it made
N. Cankaya · Substack (Naci Cankaya)
2025CBlog / article1
Verification for International AI Governance
B. Harack et al. · Oxford Martin AI Governance Initiative
2025BTechnical report8
Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment
M. Baker et al. · RAND Corporation
2025BTechnical report20
Verifying LLM Inference to Detect Model Weight Exfiltration
R. Rinberg et al. · arXiv
2025BPreprint9
WireTap: Breaking Server SGX via DRAM Bus Interposition
A. Seto et al. · 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS '25)
2025APeer-reviewed1
Zkonduit EZKL Security Assessment
F. Casal et al. · Trail of Bits (prepared for Zkonduit Inc.)
2025BTechnical report1
Computing Power and the Governance of Artificial Intelligence
G. Sastry et al. · arXiv
2024BPreprint10
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Y. Gao et al. · 2024 IEEE Symposium on Security and Privacy
2024APeer-reviewed2
DiLoCo: Distributed Low-Communication Training of Language Models
A. Douillard et al. · ICML 2024 Workshop on Advancing Neural Network Training (WANT)
2024BPreprint2
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
U. Anwar et al. · Transactions on Machine Learning Research
2024APeer-reviewed
Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
L. Heim et al. · Oxford Martin AI Governance Initiative
2024BTechnical report1
Hardware-Enabled Governance Mechanisms: Developing Technical Solutions to Exempt Items Otherwise Classified Under Export Control Classification Numbers 3A090 and 4A090
G. Kulp et al. · RAND Corporation
2024BTechnical report12
Input-Dependent Power Usage in GPUs
T. Gregersen et al. · SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis (Sustainable Supercomputing workshop), pp. 1872–1877
2024BPreprint1
Location Verification for AI Chips
A. Brass & O. Aarne · Institute for AI Policy and Strategy
2024BTechnical report5
Preventing model exfiltration with upload limits
R. Greenblatt · AI Alignment Forum
2024CForum / discussion1
Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing
O. Aarne et al. · Center for a New American Security
2024BTechnical report7
Securing AI Model Weights: Preventing Theft and Misuse of Frontier Models
S. Nevo et al. · RAND Corporation
2024BTechnical report3
Software-Based Memory Erasure with Relaxed Isolation Requirements
S. Bursuc et al. · 2024 IEEE 37th Computer Security Foundations Symposium (CSF 2024)
2024APeer-reviewed3
Trustless Audits without Revealing Data or Models
S. Waiwitlikhit et al. · 41st International Conference on Machine Learning (ICML 2024)
2024APeer-reviewed3
Verifiable evaluations of machine learning models using zkSNARKs
T. South et al. · arXiv
2024BPreprint1
Verification methods for international AI agreements
A. R. Wasil et al. · arXiv
2024BPreprint8
Zero-Knowledge Proofs of Training for Deep Neural Networks
K. Abbaszadeh et al. · 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS 2024), pp. 4316-4330
2024APeer-reviewed2
zkllm-ccs2024: code for zkLLM: Zero Knowledge Proofs for Large Language Models
H. Sun · GitHub; archived on Zenodo
2024BCode3
zkLLM: Zero Knowledge Proofs for Large Language Models
H. Sun et al. · 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS 2024)
2024APeer-reviewed8
ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs
B.-J. Chen et al. · 19th European Conference on Computer Systems (EuroSys 2024)
2024APeer-reviewed1
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters
R. Joud et al. · 21st International Conference on Smart Card Research and Advanced Applications (CARDIS 2022), LNCS 13820, pp. 45–65
2023APeer-reviewed
Confidential Computing on NVIDIA H100 GPUs for Secure and Trustworthy AI
E. Apsey et al. · NVIDIA Technical Blog
2023CBlog / article2
Exploration of secure hardware solutions for safe AI deployment
Future of Life Institute · Future of Life Institute
2023CBlog / article2
ImpedanceVerif: On-Chip Impedance Sensing for System-Level Tampering Detection
T. Mosavirik et al. · IACR Transactions on Cryptographic Hardware and Embedded Systems, 2023(1), 301–325
2023APeer-reviewed1
International Governance of Civilian AI: A Jurisdictional Certification Approach
R. Trager et al. · Centre for the Governance of AI
2023BTechnical report
Part-time Power Measurements: nvidia-smi's Lack of Attention
Z. Yang et al. · arXiv
2023BPreprint2
Proof-of-Learning is Currently More Broken Than You Think
C. Fang et al. · 8th IEEE European Symposium on Security and Privacy (EuroS&P 2023)
2023APeer-reviewed2
Remote ATtestation procedureS (RATS) Architecture (RFC 9334)
H. Birkholz et al. · Internet Engineering Task Force (RATS Working Group)
2023BTechnical report4
SAGE: Software-based Attestation for GPU Execution
A. Ivanov et al. · 2023 USENIX Annual Technical Conference (USENIX ATC 23), pp. 485–499
2023APeer-reviewed1
Tools for Verifying Neural Models' Training Data
D. Choi et al. · Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
2023APeer-reviewed2
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
Y. Shavit · arXiv
2023BPreprint20
"Adversarial Examples" for Proof-of-Learning
R. Zhang et al. · 2022 IEEE Symposium on Security and Privacy (SP)
2022APeer-reviewed1
Anti-Tamper Radio: System-Level Tamper Detection for Computing Systems
P. Staat et al. · 2022 IEEE Symposium on Security and Privacy
2022APeer-reviewed1
Common Terminology for Confidential Computing
Confidential Computing Consortium · Confidential Computing Consortium
2022BTechnical report2
ZKProof Community Reference
D. Benarroch et al. · ZKProof
2022BTechnical report4
Proof-of-Learning: code for Proof-of-Learning: Definitions and Practice
CleverHans Lab · GitHub
2021BCode1
Proof-of-Learning: Definitions and Practice
H. Jia et al. · 42nd IEEE Symposium on Security and Privacy
2021APeer-reviewed1
Detecting Covert Cryptomining Using HPC
A. Gangwal et al. · Cryptology and Network Security – CANS 2020, LNCS 12579, pp. 344–364
2020APeer-reviewed1
Secure Physical Enclosures from Covers with Tamper-Resistance
V. Immler et al. · IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019(1), 51–96
2019APeer-reviewed1
Platform Firmware Resiliency Guidelines (NIST SP 800-193)
A. Regenscheid · National Institute of Standards and Technology
2018AGovernment document1
The Past, Present, and Future of Physical Security Enclosures: From Battery-Backed Monitoring to PUF-Based Inherent Security and Beyond
J. Obermaier & V. Immler · Journal of Hardware and Systems Security
2018APeer-reviewed1
Proofs of Useful Work
M. Ball et al. · IACR Cryptology ePrint Archive 2017/203
2017BPreprint2
TCG Glossary
Trusted Computing Group · Trusted Computing Group
2017BDocumentation2
Proofs of Space
S. Dziembowski et al. · CRYPTO 2015 (IACR Cryptology ePrint Archive 2013/796)
2015APeer-reviewed5
Tamper-Indicating Enclosures, A Current Survey
H. A. Smartt & Z. N. Gastelum · Sandia National Laboratories, SAND2015-4251C
2015BTechnical report1
IBM 4765 Cryptographic Coprocessor Security Module: Security Policy
IBM Corporation · NIST Cryptographic Module Validation Program
2012BTechnical report1
Secure Code Update for Embedded Devices via Proofs of Secure Erasure
D. Perito & G. Tsudik · Computer Security – ESORICS 2010, LNCS 6345, pp. 643–662
2010APeer-reviewed2
On the Difficulty of Software-Based Attestation of Embedded Devices
C. Castelluccia et al. · ACM Conference on Computer and Communications Security (CCS 2009)
2009APeer-reviewed1
SWATT: SoftWare-based ATTestation for Embedded Devices
A. Seshadri et al. · IEEE Symposium on Security and Privacy 2004
2004APeer-reviewed1
Guidelines for Writing RFC Text on Security Considerations (RFC 3552, BCP 72)
E. Rescorla et al. · Internet Engineering Task Force
2003AStandard1
Security Requirements for Cryptographic Modules (FIPS PUB 140-2)
National Institute of Standards and Technology · National Institute of Standards and Technology
2001AStandard1
Tamper Detection for Safeguards and Treaty Monitoring: Fantasies, Realities, and Potentials
R. G. Johnston · The Nonproliferation Review, Spring 2001, pp. 102–114
2001APeer-reviewed1
Physical Security and Tamper-Indicating Devices
R. G. Johnston & A. R. E. Garcia · Los Alamos National Laboratory, LA-UR-96-3827
1996BTechnical report2