Kundu et al., 2024 - Google Patents
SoK Paper: Security Concerns in Quantum Machine Learning as a ServiceKundu et al., 2024
View PDF- Document ID
- 2841860586060882715
- Author
- Kundu S
- Ghosh S
- Publication year
- Publication venue
- Proceedings of the International Workshop on Hardware and Architectural Support for Security and Privacy 2024
External Links
Snippet
Quantum machine learning (QML) is a category of algorithms that uses variational quantum circuits (VQCs) to solve machine learning tasks. Recent works have shown that QML models can effectively generalize from limited training data samples. This capability has led to an …
- 238000010801 machine learning 0 title abstract description 24
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/70—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer
- G06F21/71—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information
- G06F21/74—Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information operating in dual or compartmented mode, i.e. at least one secure mode
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Nisq quantum computing: A security-centric tutorial and survey [feature] | |
| Kumar et al. | A review on double spending problem in blockchain | |
| Kundu et al. | Security aspects of quantum machine learning: Opportunities, threats and defenses | |
| Ghosh et al. | A primer on security of quantum computing hardware | |
| US20240126891A1 (en) | Predicting and Quantifying Weaponization of Software Weaknesses | |
| Choudhury et al. | Crosstalk-induced side channel threats in multi-tenant nisq computers | |
| Kundu et al. | SoK Paper: Security Concerns in Quantum Machine Learning as a Service | |
| Yan et al. | Towards explainable model extraction attacks | |
| Zheng et al. | A new malware detection method based on vmcadr in cloud environments | |
| US12015691B2 (en) | Security as a service for machine learning | |
| Altaha et al. | Machine Learning in Malware Analysis: Current Trends and Future Directions. | |
| Gupta et al. | Detection of vulnerabilities in blockchain smart contracts: a review | |
| Isik et al. | Neurosec: Fpga-based neuromorphic audio security | |
| Weiss et al. | EZClone: Improving DNN model extraction attack via shape distillation from GPU execution profiles | |
| Upadhyay et al. | Quantum quandaries: Unraveling encoding vulnerabilities in quantum neural networks | |
| Li et al. | PUF-based intellectual property protection for CNN model | |
| Ghosh et al. | The quantum imitation game: Reverse engineering of quantum machine learning models | |
| Heredge et al. | Characterizing privacy in quantum machine learning | |
| Upadhyay et al. | Quantum data breach: Reusing training dataset by untrusted quantum clouds | |
| Ranjani et al. | Sparse attention with residual pyramidal depthwise separable convolutional based malware detection with optimization mechanism | |
| Joshi et al. | Quantum ai algorithm development for enhanced cybersecurity: A hybrid approach to malware detection | |
| Appiah | MultiVul-GCN: automatic smart contract vulnerability detection using multi-graph convolutional networks | |
| AlShawi | Applying data mining techniques to improve information security in the cloud: a single cache system approach | |
| Yuan et al. | Secure integrated circuit design via hybrid cloud | |
| Alyami et al. | Implementing integrity assurance system for big data |