The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index; none of the 15 sources its claims cite is checked for support yet.
SecureBoost: A Lossless Federated Learning Framework
Open accessAbstract
The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this article, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned dataset. An advantage of SecureBoost is that it provides the same level of accuracy as the non -privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other nonfederated gradient tree-boosting algorithms that require centralized data, and thus, it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
SecureBoost is a vertical federated gradient tree-boosting framework that uses entity alignment and Paillier homomorphic encryption to train jointly across parties without sharing raw data or labels. The authors prove it is lossless relative to centralized XGBoost under identical initialization and hyperparameters, and propose RL-SecureBoost to reduce leakage from the first tree.
Key findings
- SecureBoost is lossless: Theorem 1 states that SecureBoost model \(M\) and XGBoost model \(M'\) behave identically if identically initialized and hyperparameterized.
- On Credit 1 (150,000 instances, 10 attributes) and Credit 2 (30,000 instances, 25 attributes), overall SecureBoost achieved ACC 0.9345, F1 0.2576, AUC 0.8461 on Credit 1 and ACC 0.8180, F1 0.4634, AUC 0.7701 on Credit 2; RL-SecureBoost achieved ACC 0.9331, F1 0.2549, AUC 0.8423 on Credit 1 and ACC 0.8179, F1 0.4650, AUC 0.7682 on Credit 2. A pairwise Wilcoxon signed-rank test at significance level 0.05 indicated RL-SecureBoost is as accurate as SecureBoost.
- RL-SecureBoost reduced leakage: mean leaf purity dropped from 0.8058 in the first tree to 0.66663 in the second tree on Credit 1, and from 0.7159 to 0.638 on Credit 2; the second tree’s mean leaf purity was just over 0.6 on both datasets.
- Scalability experiments showed runtime increased almost linearly with maximum tree depth, and sample and feature numbers contributed equally to running time.
Methods
- Vertical federated learning setting: data are partitioned by features among \(m\) parties, only the active party holds labels, and parties share a common set of users. SecureBoost first performs privacy-preserving entity alignment via inter-database intersection, then collaboratively builds gradient-boosted trees using Paillier additive homomorphic encryption; passive parties compute encrypted gradient statistics locally, and the active party decrypts aggregated values to find global splits.
- Experiments used two public credit datasets, with 2/3 for training and the remainder for testing, vertically split into two halves across two parties. All methods used maximum tree depth 3, subsample fraction 0.8, learning rate 0.3, and Paillier encryption with 512-bit keys on a machine with 8 GB RAM and Intel Core i5-7200u CPU.
- Analyses compared convergence with GBDT and XGBoost, varied maximum depth over {3,4,5,6,7,8}, varied feature number over {50,500,1000,5000} and sample number over {5000,10000,30000}, and evaluated RL-SecureBoost versus SecureBoost using accuracy, F1-score, AUC, and a Wilcoxon signed-rank test.
Limitations
- The paper states that the active party learns the instance space for each split, which party is responsible for each decision, and all possible values of \(g_l, g_r, h_l, h_r\) during learning; the latter can be avoided with secure multi-party computation for encrypted comparison, but this significantly affects learning efficiency.
- The paper states that passive-party leakage is directly related to the first tree’s leaf purity, and Theorem 2 shows that the first tree’s leaf purity can be inferred from leaf weights. RL-SecureBoost stores decision-tree leaves at the active party and builds the first tree independently using only active-party features, but residual information may still reveal information, though it decreases as first-tree purity increases.
- Evident from the text: the empirical evaluation is limited to two public credit datasets and a two-party vertical split, with no multi-party or non-credit-domain experiments reported.
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Claims the paper makes
27 Own interpretation · 8 Not checked yet · 6 Own result · 2 No basis given · 1 Sources not found
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We make similar observations on both Figure 5 (b) and Figure 5 (c), which imply that sample and feature numbers contribute equally to running time.”
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“Moreover, as we experimentally demonstrate in Section 8, RL-SecureBoost performs identically as SecureBoost in terms of prediction accuracy.”
- Also stated in: § 8.2 Performance of RL-SecureBoost · p. 7 · hedged
“As observed, RL-SecureBoost performs equally well compared to SecureBoost in almost all cases.”
- Also stated in: § 8.2 Performance of RL-SecureBoost · p. 7 · hedged
“The comparison results indicate that RL-SecureBoost is as accurate as SecureBoost, with a significance level of 0 . 05 .”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Also stated in: § 8.2 Performance of RL-SecureBoost · p. 7 · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As depicted in Figure 5 (a), the runtime increases almost linearly with the maximum depth of individual trees”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, the mean leaf purity of the second tree is just over 0 . 6 on both datasets”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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In the paper
“According to Table 1, the mean leaf purity decreases significantly from the first to the second tree on both datasets”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Unlike sample-partitioned/horizontal FL, the vertical FL setting requires a more complex mechanism to decompose the loss function at each party [5], [15], [16].”
What the sources say
- Not checked yetcites [5] [5] S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne, 'Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption,' arXiv preprint arXiv:1711.10677 , 2017. - source
- Not checked yetcites [15] [15] J. Vaidya, 'A survey of privacy-preserving methods across vertically partitioned data,' in Privacy-preserving data mining . Springer, 2008, pp. 337-358. - source
- Not checked yetcites [16] [16] J. Vaidya and C. Clifton, 'Privacy-preserving decision trees over vertically partitioned data,' in IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 2005, pp. 139-152. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In addition, they can only handle discrete data, which is less practical for real-life scenarios.”
What the sources say
- Not checked yetcites [16] [16] J. Vaidya and C. Clifton, 'Privacy-preserving decision trees over vertically partitioned data,' in IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 2005, pp. 139-152. -
- Not checked yetcites [28] [28] J. Vaidya, C. Clifton, M. Kantarcioglu, and A. S. Patterson, 'Privacypreserving decision trees over vertically partitioned data,' ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 2, no. 3, p. 14, 2008. -
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Following observation (1), we can see that passive parties can determine their locally optimal split with only its local data and g i , h i , which motivates us to follow such method to decompose learning task at each party.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Given Theorem 3, we prove that RL-SecureBoost is secure as long as its first tree learns enough information to mask the actual label with residuals.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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In the paper
“However, according to observation (2), g i and h i should be regarded as sensitive data, since they are able to disclose class label information to passive parties.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 10
- Probably not a substantial claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As can be observed from Figure 4, SecureBoost shows a similar learning curve with other non-federated methods on the training dataset and even performs slightly better than others on the test dataset.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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In the paper
“In this way, the active party learns only the optimal g l , g r , h l , h r per party; on the other hand, this significantly affects the efficiency during learning.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition, data exchange under DP still requires that data change hands between organizations, which may not be allowed by strict laws like GDPR.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We show that the SecureBoost framework is as accurate as other non-federated gradient tree-boosting algorithms that require centralized data and thus it is highly scalable and practical for industrial applications such as credit risk analysis.”
boosted- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, our method guarantees better protection to the data and can be easily applied to continuous data.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“However, DP only prevents user-data leakage to a certain degree and cannot completely rule out the identity of an individual.”
What the sources say
- Not checked yetcites [7] [7] C. Dwork, A. Roth et al. , 'The algorithmic foundations of differential privacy,' Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3-4, pp. 211-407, 2014. -
- Not checked yetcites [8] [8] C. Dwork, 'Differential privacy: A survey of results,' in International Conference on Theory and Applications of Models of Computation . Springer, 2008, pp. 1-19. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The former seems unavoidable in this setting, unless one is willing to severely increase the overhead during the inference phase.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With RL-SecureBoost , the active party learns the first tree independently based only on its own features which fully protects the instance space of its leaves.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SecureBoost avoids revealing data records held by each of the parties to others during training and inference thus protecting the privacy of individual parties' data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, the DP method is lossy in machine learning in that models built after noise is injected may perform unsatisfactorily in prediction accuracy.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The active party is in an advantageous position with SecureBoost as it learns the instance space for each split and which party is responsible for the decision at each node.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we stress that there is some leakage that can be inferred during the protocol execution which is quite different for passive vs. active parties.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Many data owners do not have sufficient amount of data to build high-quality models on their own.
“In reality, many data owners do not have sufficient amount of data to build high-quality models.”
The paper relies on this without support.
Different organizations need to collaborate while building machine learning models.
“the need for different organizations to collaborate while building machine learning models still stays strong.”
The paper relies on this without support.
It is still a challenge to allow different data owners to collaboratively build high-quality machine learning models while at the same time protecting user data privacy and confidentiality.
“So far, it is still a challenge to allow different data owners to collaboratively build high-quality machine learning models while at the same time protecting user data privacy and confidentiality.”
The authors state this as an assumption.
For Theorem 2, all initial predictions y_i(0) are initialized to a constant a with 0 < a < 1.
“Suppose we initialize all ˆ y i (0) as a where 0 < a < 1 .”
The authors state this as an assumption.
For Theorem 2, when the number of instances associated with a leaf n_j is relatively big, λ can be ignored in the leaf weight formula.
“When n j is relatively big, we can ignore λ in -∑ i ∈ I j g i ∑ i ∈ I j h i + λ and rewrite the weight of leaf j as”
The authors state this as an assumption.
For Theorem 3, when there are a large number of samples but a small number of leaf nodes k, Eq.(10) approximates Eq.(9).
“When we have a large amount of samples but small number of leave nodes k , we can use the following equation to approximates Eq.( 9).”
The authors state this as an assumption.
For Theorem 3, positive and negative samples are balanced (N_n = N_p).
“When the positive samples and negative samples are balanced, N n = N p , we have”
The authors state this as an assumption.
For Theorem 3, a is properly initialized, for instance a = 0.5.
“When we have proper initialization of a , for instance a = 0 . 5 , θ j ∗ = 0 . 5 .”
The paper relies on this without support.
Instances associated with the same leaf strongly indicate they belong to the same class.
“Note that the instances that are associated with the same leaf strongly indicates they belong to the same class.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“A recent work proposed a more practical data-free approach named FedSyn for one-shot FL framework with heterogeneity [191].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [191]
In the citing paper
“FedSyn is the first method that can be practically applied to various real-world applications due to the following benefits: (1) FedSyn requires no additional information (except the model parameters) to be transferred between participants and the server; (2) FedSyn does not require any auxiliary dataset for training; (3) FedSyn is the first to consider both model and statistical heterogeneities”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [191]
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Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 7 SECURITY DISCUSSION · p. 6
“we know the numerator has to be positive and the denominator has to be negative.”
§ 7 SECURITY DISCUSSION · p. 6
“Note that the denominator is ∑ x 2 and the numerator is ( ∑ x ) 2 where x ∈ [0 , 1] .”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ SecureBoost: A Lossless Federated Learning Framework · p. 1
“reveals no information of each private data provider.”
§ 7 SECURITY DISCUSSION · p. 5
“The active party is in an advantageous position with SecureBoost as it learns the instance space for each split and which party is responsible for the decision at each node. Also, it learns all the possible values of g l , g r and h l , h r , during learning.”
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“For privacy preservation, SecureBoost [56] builds multi-part reinforcement trees with an encryption strategy between different nodes.”
BackgroundMethodPrivacy and Robustness in Federated Learning: Attacks and Defenses
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Metadata
- Authors
- Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q.
- Affiliations
- University of California, Los Angeles (US)
- WeBank, Shenzhen, China
- Hong Kong University of Science and Technology (HK)
- Venue
- IEEE Intelligent Systems
- Published
- 2021-11-01
- Volume
- 36
- Issue
- 6
- Pages
- 87-98
- ISSN
- 1541-1672 (ISSN-L)
- 1941-1294
- OpenAlex
- W3164712068
- Semantic Scholar
- f71687796caa7d2a5d68406b53b51cb9ed57f586
- Cited by
- 669
- References
- 29
- Citations by year
- 2026: 68 · 2025: 105 · 2024: 145 · 2023: 111 · 2022: 111 · 2021: 66
- FWCI
- 38.11
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Intelligent Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2018AAA0101100)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- federated learning, privacy-preserving machine learning, gradient boosting decision tree, gradient boosting, entity alignment, vertical partitioning, information leakage, GDPR, credit risk assessment
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