Privacy challenges in extreme gradient boosting

Scientists describe the use of privacy-preserving machine learning to address privacy challenges in XGBoost training and prediction.

(Editor’s note: This is the fourth in a series of articles Amazon Science is publishing related to the science behind products and services from companies in which the Amazon Alexa Fund has invested. The Alexa Fund completed a strategic investment in Inpher, Inc., earlier this year; the New York and Swiss-based company develops privacy-preserving machine learning and analytics solutions that help organizations unlock the value of sensitive, siloed data to enable secure collaboration across organizations. This article is co-authored by Dimitar Jetchev, the cofounder and chief technology officer of Inpher, and Joan Feigenbaum, an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale University.)

Joan Feigenbaum and Dimitar Jetchev
Dimitar Jetchev (left), the cofounder and chief technology officer of Inpher, and Joan Feigenbaum, the Grace Murray Hopper professor of computer science at Yale University, and an Amazon Scholar, describe the use of privacy-preserving machine learning to address privacy challenges in XGBoost training and prediction.
Credit: Glynis Condon

Machine learning (ML) is increasingly important in a wide range of applications, including market forecasting, service personalization, voice and facial recognition, autonomous driving, health diagnostics, education, and security analytics. Because ML touches so many aspects of our lives, it’s of vital concern that ML systems protect the privacy of the data used to train them, the confidential queries submitted to them, and the confidential predictions they return.

Privacy protection — and the protection of organizations’ intellectual property — motivates the study of privacy-preserving machine learning (PPML). In essence, the goal of PPML is to perform machine learning in a manner that does not reveal any unnecessary information about training-data sets, queries, and predictions.

Suppose, for example, that schools supplied encrypted student records to educational researchers who used them to train ML models. Suppose further that students, parents, teachers, and other researchers could feed encrypted queries to the models and receive encrypted predictions in return. By taking advantage of PPML techniques in this manner, all of the participants could mine the knowledge contained in educational-record databases without compromising the privacy of the data subjects or the data users.

PPML is a very active area, with an eponymous annual workshop and many strong papers in general-ML and security venues. Techniques have been developed for privacy-preserving training and prediction on a wide range of ML model types, e.g., neural nets, decision trees, and logistic-regression formulae.

In the sections below, we describe PPML methods for training and prediction in extreme gradient boosting.

Training

Gradient boosting is an ML method for regression and classification problems that yields a set of prediction trees, typically classification and regression trees (CARTs), which together constitute a model. A CART is a generalization of a binary decision tree; while a binary tree produces a binary output, classifying each input query as a “yes” or “no,” a CART assigns each input query a (real) numerical score.

Interpretation of scores is application dependent. If v is a query, then each CART in the model assigns a score to v, and the final prediction of the model on input v is the sum of these scores. In some applications, the softmax function may be used instead of sum to produce a probability distribution over the predicted output classes.

Extreme gradient boosting (XGBoost) is an optimized, distributed, gradient-boosting framework that is efficient, portable, and flexible. In this section, we consider confidentiality of training data in the creation of XGBoost models for disease prediction — specifically, for prediction of multiple sclerosis (MS).

Early diagnosis and treatment of MS is crucial to prevent degenerative progression of the disease and patient disabilities. A recent paper proposes an early-diagnosis method that applies XGBoost to electronic health records and uses three types of features: diagnostic, epidemiologic, and laboratory.

How cryptographic computing can accelerate the adoption of cloud computing

In a previous Amazon Science article, Joan Feigenbaum reviewed secure multiparty computation and privacy-preserving machine learning – two cryptographic techniques employed to address cloud-computing privacy concerns and accelerate enterprise cloud adoption.

The presence of another neurological disease (e.g., acute disseminated encephalomyelitis (ADEM)) is an example of a diagnostic feature. Epidemiologic features include age, gender, and total number of visits to a hospital. Two more features that are discovered by lab tests are used in the model and referred to as laboratory features: hyperlipidemia (abnormally elevated levels of any or all lipids) and hyperglycemia (elevated blood sugar). The proposed XGBoost model significantly outperforms other ML techniques (including naïve Bayes methods, k-nearest neighbor, and support vector machines) that have been proposed for early diagnosis of MS.

Collecting a sufficient number of high-quality data samples and features to train such a diagnostic model is quite challenging, because the data reside in different private locations. The training data can be split in different ways among these locations: horizontally split, vertically split, or both.

If the private data sources contain samples with the same feature set (as would be the case if, say, the same features are extracted from health records residing in different hospitals), the dataset is said to be horizontally split. The other extreme — vertically split data — occurs when a private data source contributes a new feature for all of the training samples. For example, a health-insurance company could supply reimbursement receipts for past medication (the new feature) to complement the features in clinical health records. In these scenarios, aggregating the training data on a central server violates GDPR regulations.

The figure below illustrates one possible CART in the trained model. The weights at the leaves might indicate probabilities of MS resulting from the various paths from root to leaf.

Classification and regression trees (CART)

Research on privacy-preserving training of XGBoost models for prediction of MS uses two distinct techniques: secure multiparty computation (SMPC) and privacy-preserving federated learning (PPFL). We briefly describe both of them here.

An SMPC protocol enables several parties, each of whom holds a private input, to jointly evaluate a publicly known function on these inputs without revealing anything about the inputs except what is implied by the output of the function. Private inputs are secret shared among the parties, e.g., via additive secret sharing, in which each owner of a private input v generates random “shares” that add up to v.

For instance, suppose that Alice’s private input is v = 5. She can secret share it among herself, Bob, and Charlie by generating two random integers SBob =125621 and SCharlie = 56872, sending Bob’s share to him and Charlie’s to him, and keeping SAlice = v - SBob - SCharlie = -182488. Unless an adversary controls all three parties, he cannot learn anything about Alice’s private input v.  
  
In an execution of an SMPC protocol, the inputs to each elementary operation (addition or multiplication) are secret shared, and the output of the operation is a set of secret shares of the result. We say that a secret-shared value y (which may be the final output of the computation) is revealed to party P if all the parties send their shares to P, thus enabling P to reconstruct y. Further discussion of SMPC and its relevance to cloud computing can be found here and in Inpher’s Secret Computing Explainer Series.

A recent paper by researchers at Inpher proposes an SMPC protocol, called XORBoost, for privacy-preserving training of XGBoost models. It improves the state of the art by several orders of magnitude and ensures that

  • The CARTs computed by the protocol are secret shared among the training-data owners and revealed only to a designated party, namely the data analyst.
  • The training algorithm not only protects the input data but also reveals no information about the paths in the CARTs taken by any of the training samples. 
  • XORBoost supports both numerical and categorical features, thus providing enough flexibility and generality to support the above model.    

XORBoost works well for training datasets of reasonable size — hundreds of thousands of samples and hundreds of features. However, many real-world applications require training on more than a million samples. To achieve that type of scale, one can use federated learning (FL), which is an ML technique used to train a model on data samples held locally by multiple, decentralized edge devices without requiring the devices to exchange the samples.

FL differs from XORBoost mainly in that FL does not perform the entire training exercise on secret-shared values. Rather, each device trains a local model on its local data samples and sends its local model to one or more servers for aggregation. The aggregation protocol typically uses simple operations such as sum, average, and oblivious comparisons but no complex optimization.

If the server receives the plaintext local-model updates from all of the devices, it could, in principle, recover the local training-data samples using model-inversion attacks. SMPC and other privacy-preserving computational techniques can be applied to aggregate local models without revealing them to the server. See the diagram below for the overall architecture. 

XORBoost architecture

Prediction

PPXGBoost is a privacy-preserving version of XGBoost prediction. More precisely, it is a system that supports encrypted queries to encrypted XGBoost models. PPXGBoost is designed for applications that start by training a plaintext model Ω on a suitable training-data set and then create, for each user U, a personalized, encrypted version ΩU of the model to which U will submit encrypted queries and from which she will receive encrypted results. 

PPXGBoost system architecture

The PPXGBoost system architecture is shown in the figure above. On the client side, there is an app with which a user encrypts queries and decrypts results. On the server side, there is a module called Proxy that runs in a trusted environment and is responsible for setup (i.e., creating, for each authorized user, a personalized, encrypted model and a set of cryptographic keys) and an ML module that executes the encrypted queries. PPXGBoost uses two specialized types of encryption schemes (symmetric-key, order-preserving encryption and public-key, additive, homomorphic encryption) to encrypt models and evaluate encrypted queries. Each user is issued keys for both schemes during the setup phase.

Note that PPXGBoost is a natural choice for researchers, clinicians, and patients who wish to make disease predictions repeatedly as the patients’ circumstances change. Potentially relevant changes include exposure to new environmental factors, experimental treatment for another condition, or simply aging. An individual patient can create a personalized, encrypted version of a disease-prediction model and store it on a server owned by the medical center at which he is receiving treatment. Patient and physician can then use it to monitor, in a privacy-preserving manner, changes in the patient’s likelihood of contracting the disease.

Conclusion

We have described the use of PPML to address privacy challenges in XGBoost training and prediction. In a future post, we will elaborate on how privacy-preserving federated learning enables researchers to train more-complex ML models on millions of samples stored on hundreds of thousands of devices.

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Amazon launched the Generative AI Innovation Center (GenAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GenAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GenAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NY, USA | San Francisco, CA, USA | San Jose, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
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Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: - Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries - Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them - Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NJ, USA | New York, NY, USA | San Francisco, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA