ZTE Communications ›› 2023, Vol. 21 ›› Issue (1): 15-24.DOI: 10.12142/ZTECOM.202301003
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ZHANG Weiting1, LIANG Haotian2, XU Yuhua2, ZHANG Chuan2()
Received:
2022-11-01
Online:
2023-03-25
Published:
2023-03-22
Contact:
ZHANG Chuan
About author:
ZHANG Weiting received his PhD degree in communication and information systems from Beijing Jiaotong University, China in 2021. From Nov. 2019 to Nov. 2020, he was a visiting PhD student with the BBCR Group, Department of Electrical and Computer Engineering, University of Waterloo, Canada. He is currently an associate professor with the School of Electronic and Information Engineering, Beijing Jiaotong University. His research interests include industrial Internet of Things, edge intelligence, and machine learning for wireless networks.|LIANG Haotian received his BS degree from Lanzhou University, China in 2022. He is currently working towards his master’s degree in the School of Cyberspace Science and Technology, Beijing Institute of Technology, China. His research interests include machine learning security, Internet of Things security, and cloud security.|XU Yuhua is currently an undergraduate student in School of Computer Science and Technology, Beijing Institute of Technology, China. She is currently working at the research laboratory of advanced network and data security at the School of Cyberspace Science and Technology, Beijing Institute of Technology. Her research interests include applied cryptography and blockchain.|ZHANG Chuan(Supported by:
ZHANG Weiting, LIANG Haotian, XU Yuhua, ZHANG Chuan. Reliable and Privacy-Preserving Federated Learning with Anomalous Users[J]. ZTE Communications, 2023, 21(1): 15-24.
User Privacy Preservation | Robust to User Instability | Support for Anomalous Users | Collusion Resistance | Server Setting | |
---|---|---|---|---|---|
PPDL[ | √ | Single-cloud | |||
PPML[ | √ | √ | √ | Single-cloud | |
SecProbe[ | √ | √ | √ | Single-cloud | |
PPFDL[ | √ | √ | √ | Two non-colluding clouds | |
RPPFL | √ | √ | √ | √ | Single-cloud |
Table 1 Comparison of RPPFL and other existing works
User Privacy Preservation | Robust to User Instability | Support for Anomalous Users | Collusion Resistance | Server Setting | |
---|---|---|---|---|---|
PPDL[ | √ | Single-cloud | |||
PPML[ | √ | √ | √ | Single-cloud | |
SecProbe[ | √ | √ | √ | Single-cloud | |
PPFDL[ | √ | √ | √ | Two non-colluding clouds | |
RPPFL | √ | √ | √ | √ | Single-cloud |
Notation | Meaning |
---|---|
A large positive integer | |
The set of integers modulo | |
The multiplicative group of reversible elements of | |
The number of users | |
The number of the selected users | |
The number of gradient types | |
A big integer of the magnitude of 10 | |
The | |
The integer corresponding to the enlargement of | |
The aggregated result of the | |
The reliability (indicates the data quality) of the user | |
The coefficient used to amplify users’ reliability | |
The secret key of the selected user | |
The secret key of the aggregation server | |
The ciphertext encrypted by a public key | |
The random value selected by the user |
Table 2 Frequently used notations
Notation | Meaning |
---|---|
A large positive integer | |
The set of integers modulo | |
The multiplicative group of reversible elements of | |
The number of users | |
The number of the selected users | |
The number of gradient types | |
A big integer of the magnitude of 10 | |
The | |
The integer corresponding to the enlargement of | |
The aggregated result of the | |
The reliability (indicates the data quality) of the user | |
The coefficient used to amplify users’ reliability | |
The secret key of the selected user | |
The secret key of the aggregation server | |
The ciphertext encrypted by a public key | |
The random value selected by the user |
1 |
WANG J S, LIU Y, ZHANG W T, et al. ReLFA: resist link flooding attacks via renyi entropy and deep reinforcement learning in SDN-IoT [J]. China communications, 2022, 19(7): 157–171. DOI: 10.23919/JCC.2022.07.013
DOI |
2 |
KANG J W, LI X D, NIE J T, et al. Communication-efficient and cross-chain empowered federated learning for artificial intelligence of things [J]. IEEE transactions on network science and engineering, 2022, 9(5): 2966–2977. DOI: 10.1109/TNSE.2022.3178970
DOI |
3 |
ZHANG W T, YANG D, WU W, et al. Spectrum and computing resource management for federated learning in distributed industrial IoT [C]//Proceedings of 2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2021: 1–6. DOI: 10.1109/ICCWorkshops50388.2021.9473515
DOI |
4 |
ZHANG W T, YANG D, WU W, et al. Optimizing federated learning in distributed industrial IoT: A multi-agent approach [J]. IEEE journal on selected areas in communications, 2021, 39(12): 3688–3703. DOI: 10.1109/JSAC.2021.3118352
DOI |
5 |
PENG H X, SHEN X M. Multi-agent reinforcement learning based resource management in MEC- and UAV-assisted vehicular networks [J]. IEEE journal on selected areas in communications, 2021, 39(1): 131–141. DOI: 10.1109/JSAC.2020.3036962
DOI |
6 |
PENG H X, WU H Q, SHEN X S. Edge intelligence for multi-dimensional resource management in aerial-assisted vehicular networks [J]. IEEE wireless communications, 2021, 28(5): 59–65. DOI: 10.1109/MWC.101.2100056
DOI |
7 | Union European. General data protection regulation [EB/OL]. [2022-10-28]. |
8 | State of California Department of Justice. California consumer privacy act [EB/OL]. [2022-10-28]. |
9 |
SONG C Z, RISTENPART T, SHMATIKOV V. Machine learning models that remember too much [C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2017: 587–601. DOI: 10.1145/3133956.3134077
DOI |
10 | ZHU L G, LIU Z J, HAN S. Deep leakage from gradients Advances [EB/OL]. [2022-10-28]. |
11 | ZHAO B, MOPURI K R, BILEN H. iDLG: improved deep leakage from gradients [EB/OL]. [2022-10-28]. |
12 | YIN H X, MALLYA A, VAHDAT A, et al. See through gradients: image batch recovery via gradinversion [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021: 16332–16341 |
13 |
ZHANG C, ZHAO M Y, ZHU L H, et al. FRUIT: a blockchain-based efficient and privacy-preserving quality-aware incentive scheme [J]. IEEE journal on selected areas in communications, 2022, 40(12): 3343–3357. DOI: 10.1109/JSAC.2022.3213341
DOI |
14 |
OUADRHIRI A E, ABDELHADI A. Differential privacy for deep and federated learning: a survey [J]. IEEE access, 2022, 10: 22359–22380. DOI: 10.1109/ACCESS.2022.3151670
DOI |
15 |
PEYVANDI A, MAJIDI B, PEYVANDI S, et al. Privacy-preserving federated learning for scalable and high data quality computational-intelligence-as-a-service in Society 5.0 [J]. Multimedia tools and applications, 2022, 81(18): 25029–25050. DOI: 10.1007/s11042-022-12900-5
DOI |
16 |
PHONG L T, AONO Y, HAYASHI T, et al. Privacy-preserving deep learning via additively homomorphic encryption [J]. IEEE transactions on information forensics and security, 2018, 13(5): 1333–1345. DOI: 10.1109/TIFS.2017.2787987
DOI |
17 |
ZHAO L C, WANG Q, ZOU Q, et al. Privacy-preserving collaborative deep learning with unreliable participants [J]. IEEE transactions on information forensics and security, 2020, 15: 1486–1500. DOI: 10.1109/TIFS.2019.2939713
DOI |
18 |
XU G W, LI H W, ZHANG Y, et al. Privacy-preserving federated deep learning with irregular users [J]. IEEE transactions on dependable and secure computing, 2022, 19(2): 1364–1381. DOI: 10.1109/TDSC.2020.3005909
DOI |
19 |
BONAWITZ K, IVANOV V, KREUTER B, et al. Practical secure aggregation for privacy-preserving machine learning [C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2017: 1175–1191. DOI: 10.1145/3133956.3133982
DOI |
20 |
MOHASSEL P, ZHANG Y P. SecureML: a system for scalable privacy-preserving machine learning [C]//Proceedings of 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 2017: 19–38. DOI: 10.1109/SP.2017.12
DOI |
21 |
ZHENG Y F, DUAN H Y, WANG C. Learning the truth privately and confidently: encrypted confidence-aware truth discovery in mobile crowdsensing [J]. IEEE transactions on information forensics and security, 2018, 13(10): 2475–2489. DOI: 10.1109/TIFS.2018.2819134
DOI |
22 |
MIAO C L, JIANG W J, SU L, et al. Cloud-enabled privacy-preserving truth discovery in crowd sensing systems [C]//Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 2015: 183–196. DOI: 10.1145/2809695.2809719
DOI |
23 |
DAMGARD I, JURIK M. A generalisation, a simplification and some applications of paillier’s probabilistic public-key system [M]. Public Key Cryptography. Berlin, Heidelberg: Springer Berlin, 2001: 119–136. DOI: 10.1007/3-540-44586-2_9
DOI |
24 |
LI Y L, GAO J, MENG C S, et al. A survey on truth discovery [J]. ACM SIGKDD explorations newsletter, 2016, 17(2): 1–16. DOI: 10.1145/2897350.2897352
DOI |
25 | SMITH V, CHIANG C K, SANJABI M, et al. Federated multi-task learning [EB/OL]. [2022-10-28]. |
26 |
WANG L P, WANG W, LI B. CMFL: mitigating communication overhead for federated learning [C]//Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2019: 954–964. DOI: 10.1109/ICDCS.2019.00099
DOI |
27 |
XU G W, LI H W, TAN C, et al. Achieving efficient and privacy-preserving truth discovery in crowd sensing systems [J]. Computers & security, 2017, 69: 114–126. DOI: 10.1016/j.cose.2016.11.014
DOI |
28 |
MADI A, STAN O, MAYOUE A, et al. A Secure Federated Learning framework using Homomorphic Encryption and Verifiable Computing [C]//Proceedings of 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS). IEEE, 2021: 1–8. DOI: 10.1109/RDAAPS48126.2021.9452005
DOI |
29 |
SHOKRI R, SHMATIKOV V. Privacy-preserving deep learning [C]//Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. ACM, 2015: 1310–1321. DOI: 10.1145/2810103.2813687
DOI |
30 |
ABADI M, CHU A, GOODFELLOW I, et al. Deep learning with differential privacy [C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016: 308–318. DOI: 10.1145/2976749.2978318
DOI |
31 |
JAYARAMAN B, WANG L X, EVANS D, et al. Distributed learning without distress: privacy-preserving empirical risk minimization [C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. ACM, 2018: 6346–6357. DOI: 10.5555/3327345.3327531
DOI |
32 |
XU G W, LI H W, LIU S, et al. VerifyNet: secure and verifiable federated learning [J]. IEEE transactions on information forensics and security, 2020, 15: 911–926. DOI: 10.1109/TIFS.2019.2929409
DOI |
33 | JAYARAMAN B, EVANS D. Evaluating differentially private machine learning in practice [EB/OL]. [2022-10-28]. |
34 |
HITAJ B, ATENIESE G, PEREZ-CRUZ F. Deep models under the GAN: information leakage from collaborative deep learning [C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2017: 603–618. DOI: 10.1145/3133956.3134012
DOI |
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