A Differentially Private Random Decision Forest Using Reliable Signal-to-Noise Ratios

Fletcher, Samuel C.; Islam, Zahidul

doi:10.1007/978-3-319-26350-2_17

Cited by 18 publications

(43 citation statements)

References 11 publications

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“…For instance, the max operator has lower sensitivity than the Gini or information gains thus leading up to higher accuracy on similar datasets and privacy levels [61,68]. In other works, the learning is adapted using RFs [83,140,146] or CRTs [20,59,60,188]. Some works abusively consider that each tree in the forest is independent, to reduce the privacy budget consumption [146].…”

Section: Differential Privacy Based Solutionsmentioning

confidence: 99%

“…Some works abusively consider that each tree in the forest is independent, to reduce the privacy budget consumption [146]. However, this is circumvented by training each tree on an independent subset of the training data and applying the parallel composition theorem [59,183]. Overall, works based on the central model consider reasonable privacy budgets (i.e., ε∈[0.1; 1.0]) and some even experiment with very low budgets (e.g., ε=0.01 for [16,20,59,114,137]).…”

Section: Differential Privacy Based Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Chatel

Pyrgelis

Troncoso-Pastoriza

et al. 2021

Proceedings on Privacy Enhancing Technologies

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Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.

show abstract

Section: Differential Privacy Based Solutionsmentioning

confidence: 99%

Section: Differential Privacy Based Solutionsmentioning

confidence: 99%

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Chatel

Pyrgelis

Troncoso-Pastoriza

et al. 2021

Proceedings on Privacy Enhancing Technologies

View full text Add to dashboard Cite

show abstract

“…Additionally, Friedman and Schuster further proposed the DiffP-C4.5 algorithm to overcome the limitation of DiffP-ID3 that it can only solve discrete attributes [13]. Rana et al [23] and Fletcher [24] proposed the methods to build differentially private random forest, which can reduce the impact of noise on model accuracy by integrating several decision trees into an ensemble.…”

Section: Related Workmentioning

confidence: 99%

A differentially private distributed data mining scheme with high efficiency for edge computing

Sun

et al. 2021

J Cloud Comp

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A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

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“…The utility-based partitioning is inspired by an observation that many DP machine learning algorithms (e.g. [5, 7, 9, 19]) have their performance related with n , ε for a dataset of n records with ε -DP. We give definition of utility-based partitioning below.…”

Section: Partitioning Mechanismsmentioning

confidence: 99%

Partitioning-Based Mechanisms Under Personalized Differential Privacy

Xiong

et al. 2017

Lecture Notes in Computer Science

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Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.

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A Differentially Private Random Decision Forest Using Reliable Signal-to-Noise Ratios

Cited by 18 publications

References 11 publications

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

A differentially private distributed data mining scheme with high efficiency for edge computing

Partitioning-Based Mechanisms Under Personalized Differential Privacy

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