# CHERI Reading list

### CHERI Publications

1. [CHERI Tech Report](https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-951.pdf)
2.

### IEEE Security and Privacy

1. [Embedding Privacy Into Design Through Software Developers: Challenges and Solutions](https://ieeexplore.ieee.org/document/9904426)
2. [Supporting Artificial Intelligence/Machine Learning Security Workers Through an Adversarial Techniques, Tools, and Common Knowledge Framework](https://ieeexplore.ieee.org/document/9994631)
3. [Memory Errors and Memory Safety: C as a Case Study](https://ieeexplore.ieee.org/document/10102611)
4. [Are Machine Learning Models for Malware Detection Ready for Prime Time?](https://ieeexplore.ieee.org/document/10102612)
5. [Trusted Execution Environments for Telecoms: Strengths, Weaknesses, Opportunities, and Threats](https://ieeexplore.ieee.org/document/10098483)
6. [Memory Errors and Memory Safety: A Look at Java and Rust](https://ieeexplore.ieee.org/document/10137364)
7. [Ransomware-Bitcoin Threat Intelligence Sharing Using Structured Threat Information Expression](https://ieeexplore.ieee.org/document/9765840)
8. [Security Verification of the OpenTitan Hardware Root of Trust](https://ieeexplore.ieee.org/document/10106105)

### IEEE Symposium on Security and Privacy

1. [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://ieeexplore.ieee.org/document/10179324)
2. [Three Birds with One Stone: Efficient Partitioning Attacks on Interdependent Cryptocurrency Networks](https://ieeexplore.ieee.org/document/10179456)
3. [Bitcoin-Enhanced Proof-of-Stake Security: Possibilities and Impossibilities](https://ieeexplore.ieee.org/document/10179426)
4. [DBREACH: Stealing from Databases Using Compression Side Channels](https://ieeexplore.ieee.org/document/10179359)
5. [SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning](https://ieeexplore.ieee.org/document/10179281)
6. [Analyzing Leakage of Personally Identifiable Information in Language Models](https://ieeexplore.ieee.org/document/10179300)
7. [Accuracy-Privacy Trade-off in Deep Ensemble: A Membership Inference Perspective](https://ieeexplore.ieee.org/document/10179463)
8. [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://ieeexplore.ieee.org/document/10179291)
9. [Vectorized Batch Private Information Retrieval](https://ieeexplore.ieee.org/document/10179329)
10. [SoK: Cryptographic Neural-Network Computation](https://ieeexplore.ieee.org/document/10179483)
11. [FLUTE: Fast and Secure Lookup Table Evaluations](https://ieeexplore.ieee.org/document/10179345)
12. ["In Eighty Percent of the Cases, I Select the Password for Them": Security and Privacy Challenges, Advice, and Opportunities at Cybercafes in Kenya](https://ieeexplore.ieee.org/document/10179410)
13. [Redeem Myself: Purifying Backdoors in Deep Learning Models using Self Attention Distillation](https://ieeexplore.ieee.org/document/10179375)
14. [Silph: A Framework for Scalable and Accurate Generation of Hybrid MPC Protocols](https://ieeexplore.ieee.org/document/10179397/)
15. [TEEzz: Fuzzing Trusted Applications on COTS Android Devices](https://ieeexplore.ieee.org/document/10179302)
16. [Half&Half: Demystifying Intel’s Directional Branch Predictors for Fast, Secure Partitioned Execution](https://ieeexplore.ieee.org/document/10179415)
17. [Practical Program Modularization with Type-Based Dependence Analysis](https://ieeexplore.ieee.org/document/10179412)
18. [SoK: Certified Robustness for Deep Neural Networks](https://ieeexplore.ieee.org/document/10179303)
19. [FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information](https://ieeexplore.ieee.org/document/10179336)
20. ["Always Contribute Back": A Qualitative Study on Security Challenges of the Open Source Supply Chain](https://ieeexplore.ieee.org/document/10179378)
21. [It’s like flossing your teeth: On the Importance and Challenges of Reproducible Builds for Software Supply Chain Security](https://ieeexplore.ieee.org/document/10179320)
22. [Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy](https://ieeexplore.ieee.org/document/10179422)
23. [Everybody’s Got ML, Tell Me What Else You Have: Practitioners’ Perception of ML-Based Security Tools and Explanations](https://ieeexplore.ieee.org/document/10179321)
24. [SegFuzz: Segmentizing Thread Interleaving to Discover Kernel Concurrency Bugs through Fuzzing](https://ieeexplore.ieee.org/document/10179398)
25. [RSFuzzer: Discovering Deep SMI Handler Vulnerabilities in UEFI Firmware with Hybrid Fuzzing](https://ieeexplore.ieee.org/document/10179421)
26. [SQUIP: Exploiting the Scheduler Queue Contention Side Channel](https://ieeexplore.ieee.org/document/10179368)
27. [DevIOus: Device-Driven Side-Channel Attacks on the IOMMU](https://ieeexplore.ieee.org/document/10179283)
28. [A Security RISC: Microarchitectural Attacks on Hardware RISC-V CPUs](https://ieeexplore.ieee.org/document/10179399)
29. [Limits of I/O Based Ransomware Detection: An Imitation Based Attack](https://ieeexplore.ieee.org/document/10179372)
30. [Characterizing Everyday Misuse of Smart Home Devices](https://ieeexplore.ieee.org/document/10179476)
31. [SecureCells: A Secure Compartmentalized Architecture](https://ieeexplore.ieee.org/document/10179472)
32. [Control Flow and Pointer Integrity Enforcement in a Secure Tagged Architecture](https://ieeexplore.ieee.org/document/10179416)
33. [EC: Embedded Systems Compartmentalization via Intra-Kernel Isolation](https://ieeexplore.ieee.org/document/10179285)
34. [Low-Cost Privilege Separation with Compile Time Compartmentalization for Embedded Systems](https://ieeexplore.ieee.org/document/10179388)
35. [One Key to Rule Them All: Secure Group Pairing for Heterogeneous IoT Devices](https://ieeexplore.ieee.org/document/10179369)
36. [Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards](https://ieeexplore.ieee.org/document/10179282/)
37. [Mew: Enabling Large-Scale and Dynamic Link-Flooding Defenses on Programmable Switches](https://ieeexplore.ieee.org/document/10179404)
38. [SyzDescribe: Principled, Automated, Static Generation of Syscall Descriptions for Kernel Drivers](https://ieeexplore.ieee.org/document/10179298/)

1. [Graphics Peeping Unit: Exploiting EM Side-Channel Information of GPUs to Eavesdrop on Your Neighbors](https://ieeexplore.ieee.org/document/9833773)
2. [Finding and Exploiting CPU Features using MSR Templating](https://ieeexplore.ieee.org/document/9833599)
3. [Hardware-Software Contracts for Secure Speculation](https://ieeexplore.ieee.org/document/9519500)
4.