How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

Lyu, Lingjuan; Li, Yitong; Nandakumar, Karthik; Yu, Jiangshan; Ma, Xingjun

doi:10.1109/tdsc.2020.3006287

Cited by 35 publications

(24 citation statements)

References 30 publications

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“…For brevity, we use ( , δ)-DP to represent ( , δ)-LDP for the rest of the paper. We remark that all the randomisation mechanisms used for CDP, including Laplace mechanism and Gaussian mechanism (Dwork and Roth, 2014), can be individually used by each party to inject noise into local data to ensure LDP before releasing (Lyu et al, 2020a;Yang et al, 2020;Lyu et al, 2020b;Sun and Lyu, 2020). In particular, we adopt Laplace Mechanism which ensures -DP with δ = 0 throughout the paper.…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Lyu¹,

He²,

Li³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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It has been demonstrated that hidden representation learned by deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy. To address this issue, we propose a novel approach called Differentially Private Neural Representation (DPNR) to preserve privacy of the extracted representation from text. DPNR utilises Differential Privacy (DP) to provide formal privacy guarantee. Further, we show that masking words via dropout can further enhance privacy. To maintain utility of the learned representation, we integrate DP-noisy representation into a robust training process to derive a robust target model, which also helps for model fairness over various demographic variables. Experimental results on benchmark datasets under various parameter settings demonstrate that DPNR largely reduces privacy leakage without significantly sacrificing the main task performance.

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Section: Introductionmentioning

confidence: 99%

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Lyu¹,

He²,

Li³

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

Self Cite

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“…Many works [33][34][35][36][37][38][39][40][41] have tried to address fairness and privacy guarantees together. Kilbertus et al [34] is one of the first proposals that addressed the need for combining fairness requirements with privacy guarantees.…”

Section: Related Workmentioning

confidence: 99%

“…The FairFace dataset [65] is a collection of ≈100 thousand facial images extracted from the YFCC-100M Flickr dataset [165]. Automated models trained on FairFace can exploit age group (age ranges of [0-2], [3][4][5][6][7][8][9], [10][11][12][13][14][15][16][17][18][19], [20][21][22][23][24][25][26][27][28][29], [30][31][32][33][34][35][36][37][38][39], [40][41][42][43][44][45][46][47][48][49], [50]…”

Section: The Datasetmentioning

confidence: 99%

“…Unfortunately few works in the literature have tried to address more than one of these problems simultaneously. Some have tried to face two of them: for example some works combine fairness with privacy [33][34][35][36][37][38][39][40][41], others [42][43][44] combine adversarial learning with fairness, fairness with explainability [45][46][47], adversarial learning with explainability [48,49], and adversarial learning with privacy [50][51][52][53]. For this reason, in this work, we drive toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design.…”

Section: Introductionmentioning

confidence: 99%

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Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Franco

Oneto

Navarin

et al. 2021

Entropy

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In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.

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“…These models allow both the private data owner and the external model owner to encrypt their data and model respectively and perform secure training and model inferencing in a distributed environment, without needing to trust any particular entity. Another approach suggested by researchers in [39] uses differentially private generative adversarial network (GAN) to generate secret tokens for detecting malicious attackers, and differentially private stochastic gradient descent to handle privacy leakage. Because FL requires distributed federated nodes to communicate in a secure and privacy-oriented way, researchers have proposed a compression technique for efficient communication, and additive HE and DP for data and model security and privacy [40] .…”

Section: Introductionmentioning

confidence: 99%

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

et al. 2020

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Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

Cited by 35 publications

References 30 publications

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

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