Differentially Private Publication of Vertically Partitioned Data

Tang, Peng; Cheng, Xiang; Su, Sen; Chen, Rui; Shao, Huaxi

doi:10.1109/tdsc.2019.2905237

Cited by 17 publications

(11 citation statements)

References 57 publications

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“…2) The second category is the works producing a PP dataset of the original dataset [56], [73], [100], [101]. Next, we will present the relevant research work based on DP to protect users' privacy before publishing a dataset.…”

Section: Privacy-preserving Datasetsmentioning

confidence: 99%

“…iii) Faithfulness: The group sizes at each level l of the hierarchy should be equal to the total count of groups G. The problem is solved using three approaches: 1) Direct optimization-based mechanism, 2) dynamic programming 3) polynomial-time mechanism by exploiting the structure of the cost tables. Tang et al [73] present a stronger privacy protection approach called differentially private latent tree (DPLT). It consists of generating a new synthetic dataset from vertically partitioned data (i.e., the dataset is shared between many data curators where each one holds some attributes of the dataset.…”

Section: Privacy-preserving Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Differential Privacy for Deep and Federated Learning: A Survey

2022

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Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, or during training, or even after releasing the trained learning model. Differential privacy (DP) is one of the main approaches proven to ensure strong privacy protection in data analysis. DP protects the users' privacy by adding noise to the original dataset or the learning parameters. Thus, an attacker could not retrieve the sensitive information of an individual involved in the training dataset. In this survey paper, we analyze and present the main ideas based on DP to guarantee users' privacy in deep and federated learning. In addition, we illustrate all types of probability distributions that satisfy the DP mechanism, with their properties and use cases. Furthermore, we bridge the gap in the literature by providing a comprehensive overview of the different variants of DP, highlighting their advantages and limitations. Our study reveals the gap between theory and application, accuracy, and robustness of DP. Finally, we provide several open problems and future research directions.

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Section: Privacy-preserving Datasetsmentioning

confidence: 99%

Section: Privacy-preserving Datasetsmentioning

confidence: 99%

Differential Privacy for Deep and Federated Learning: A Survey

2022

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“…Regarding defences against privacy attacks based on DP in VFL, Wang et al [122] propose to perturbate the intermediate outputs shared between parties in the model training phase of a Generalized Linear Model. Additionally, such perturbation removes the requirement of a learning coordinator and the necessity of costly Homomorphic Encryption schemes, as they are already private.…”

Section: Based On Differential Privacymentioning

confidence: 99%

Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges

Rodríguez-Barroso¹,

López²,

Luzón³

et al. 2022

Preprint

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Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes harder the protection against adversarial attacks and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. This study is finished leading to meditated learned lessons and challenges.

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“…Furthermore, prior VFL frameworks primarily use crypto-based technologies such as HE and SMC to ensure secure and private learning. Recent works proposed incorporating differential privacy (DP) into the training process to provide strict privacy guarantees for local data [6,42].…”

Section: Vertical Federated Learningmentioning

confidence: 99%

Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

Jiang

Zhou

Großklags

2022

Proceedings on Privacy Enhancing Technologies

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Vertical federated learning (VFL), a variant of federated learning, has recently attracted increasing attention. An active party having the true labels jointly trains a model with other parties (referred to as passive parties) in order to use more features to achieve higher model accuracy. During the prediction phase, all the parties collaboratively compute the predicted confidence scores of each target record and the results will be finally returned to the active party. However, a recent study by Luo et al. [28] pointed out that the active party can use these confidence scores to reconstruct passive-party features and cause severe privacy leakage. In this paper, we conduct a comprehensive analysis of privacy leakage in VFL frameworks during the prediction phase. Our study improves on previous work [28] regarding two aspects. We first design a general gradient-based reconstruction attack framework that can be flexibly applied to simple logistic regression models as well as multi-layer neural networks. Moreover, besides performing the attack under the white-box setting, we give the first attempt to conduct the attack under the black-box setting. Extensive experiments on a number of real-world datasets show that our proposed attack is effective under different settings and can achieve at best twice or thrice of a reduction of attack error compared to previous work [28]. We further analyze a list of potential mitigation approaches and compare their privacy-utility performances. Experimental results demonstrate that privacy leakage from the confidence scores is a substantial privacy risk in VFL frameworks during the prediction phase, which cannot be simply solved by crypto-based confidentiality approaches. On the other hand, processing the confidence scores with information compression and randomization approaches can provide strengthened privacy protection.

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Differentially Private Publication of Vertically Partitioned Data

Cited by 17 publications

References 57 publications

Differential Privacy for Deep and Federated Learning: A Survey

Differential Privacy for Deep and Federated Learning: A Survey

Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges

Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

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