Amazon wins best-paper award for protecting privacy of training data

Calibrating noise addition to word density in the embedding space improves utility of privacy-protected text.

Differential privacy is a popular technique that provides a way to quantify the privacy risk of releasing aggregate statistics based on individual data. In the context of machine learning, differential privacy provides a way to protect privacy by adding noise to the data used to train a machine learning model. But the addition of noise can also reduce model performance.

In a pair of papers at the annual meeting of the Florida Artificial Intelligence Research Society (FLAIRS), the Privacy Engineering for Alexa team is presenting a new way to calibrate the noise added to the textual data used to train natural-language-processing (NLP) models. The idea is to distinguish cases where a little noise is enough to protect privacy from cases where more noise is necessary. This helps minimize the impact on model accuracy while maintaining privacy guarantees, which aligns with the team’s mission to measurably preserve customer privacy across Alexa.

One of the papers, “Density-aware differentially private textual perturbations using truncated Gumbel noise”, has won the conference’s best-paper award.

Calibrated noise addition.gif
A simplified example of the method proposed in the researchers' award-winning paper. Noise is added to the three nearest neighbors of a source word, A, and to A itself. After noise addition, the word closest to A's original position — B — is chosen as a substitute for A.
Credit: Glynis Condon

Differential privacy says that, given an aggregate statistic, the probability that the underlying dataset does or does not contain a particular item should be virtually the same. The addition of noise to the data helps enforce that standard, but it can also obscure relationships in the data that the model is trying to learn.

In NLP applications, a standard way to add noise involves embedding the words of the training texts. An embedding represents words as vectors, such that vectors that are close in the space have related meanings. 

Adding noise to an embedding vector produces a new vector, which would correspond to a similar but different word. Ideally, substituting the new words for the old should disguise the original data while preserving the attributes that the NLP model is trying to learn. 

However, words in an embedding space tend to form clusters, united by semantic similarity, with sparsely populated regions between clusters. Intuitively, within a cluster, much less noise should be required to ensure enough semantic distance to preserve privacy. However, if the noise added to each word is based on the average distance between embeddings — factoring in the sparser regions — it may be more than is necessary for words in dense regions.

Noise calibration.png
A simplified representation of words (red dots) in an embedding space. Adding noise to a source vector (A) produces a new vector, and the nearest (green circle) embedded word (B) is chosen as a substitute. In the graph at left, adding a lot of noise to the source word produces an output word that is far away and hence semantically dissimilar. In the middle graph, however, a lot of noise is needed to produce a semantically different output. In the graph at right, the amount of noise is calibrated to the density of the vectors around the source word.

This leads us to pose the following question in our FLAIRS papers: Can we recalibrate the noise added such that it varies for every word depending on the density of the surrounding space, rather than resorting to a single global sensitivity?

Calibration techniques

We study this question from two different perspectives. In the paper titled “Research challenges in designing differentially private text generation mechanisms”, my Alexa colleagues Oluwaseyi Feyisetan, Zekun Xu, Nathanael Teissier, and I discuss general techniques to enhance the privacy of text mechanisms by exploiting features such as local density in the embedding space.  

For example, one technique deduces a probability distribution (a prior) that assigns high probability to dense areas of the embedding and low probability to sparse areas. This prior can be produced using kernel density estimation, which is a popular technique for estimating distributions from limited data samples. 

However, these distributions are often highly nonlinear, which makes them difficult to sample from. In this case, we can either opt for an approximation to the distribution or adopt indirect sampling strategies such as the Metropolis–Hastings algorithm (which is based on well-known Monte Carlo Markov chain techniques). 

Another technique we discuss is to impose a limit on how far away a noisy embedding may be from its source. We explore two ways to do this: distance-based truncation and k-nearest-neighbor-based truncation. 

Distance-based truncation simply caps the distance between the noisy embedding and its source, according to some measure of distance in the space. This prevents the addition of a large amount of noise, which is useful in the dense regions of the embedding. But in the sparse regions, this can effectively mean zero perturbation, since there may not be another word within the distance limit. 

To avoid this drawback, we consider the alternate approach of k-nearest-neighbor-based truncation. In this approach, the  words closest to the source delineate the acceptable search area. We then execute a selection procedure to choose the new word from these candidates (plus the source word itself). This is the approach we adopt in our second paper.

Nearest-neighbor search.png
A schematic of distance-based (left and middle graphs) and nearest-neighbor-based (right graph) truncation techniques. In the first graph, the blue circle represents a limit on the distance from the source word, A. Randomly adding noise produces a vector within this limit, and the output word B is selected. In the middle graph, a large amount of noise has been randomly added, but it’s truncated at the boundary of the blue circle. The right graph shows k-nearest-neighbor truncation, where a random number of neighbors (in this case, three) are selected around the source word, A. Noise is added to each of these neighbors independently, and the nearest word after noise addition — B — is chosen (see animation, above).

In “Density-aware differentially private textual perturbations using truncated Gumbel noise”, Nan Xu, a summer intern with our group in 2020 and currently a PhD student in computer science at the University of Southern California, joins us to discuss a particular algorithm in detail. 

This algorithm calibrates noise by selecting a few neighbors of the source word and perturbing the distance to these neighbors using samples from the Gumbel distribution (the rightmost graph, above). We chose the Gumbel distribution because it is more computationally efficient than existing mechanisms for differentially private selection (e.g., the exponential mechanism). The number of neighbors is chosen randomly using Poisson samples.

Together, these two techniques, when calibrated appropriately, provide the required amount of differential privacy while enhancing utility. We call the resulting algorithm the truncated Gumbel mechanism, and it better preserves semantic meanings than multivariate Laplace mechanisms, a widely used method for adding noise to textual data. (The left and middle graphs of the top figure above depict the use of Laplace mechanisms). 

In tests, we found that this new algorithm provided improvements in accuracy of up to 9.9% for text classification tasks on two different datasets. Our paper also includes a formal proof of the privacy guarantees offered by this mechanism and analyzes relevant privacy statistics. 

Our ongoing research efforts continue to improve upon the techniques described above and enable Alexa to continue introducing new features and inventions that make customers’ lives easier while keeping their data private.

Related content

US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Manager III, Data ScienceLocation: Bellevue, WashingtonPosition Responsibilities:Manage a team of data scientists working to build large-scale, technical solutions to increase effectiveness of Amazon Fulfillment systems. Define key business goals and map them to the success of technical solutions. Aggregate, analyze and model data from multiple sources to inform business decisions. Manage and quantify improvement in the customer experience resulting from research outcomes. Develop and manage a long-term research vision and portfolio of research initiatives, with algorithms and models that to be integrated in production systems. Hire and mentor junior is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, VA, Arlington
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Arlington, VirginiaPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, IL, Chicago
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist ILocation: Chicago, IllinoisPosition Responsibilities:Build the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. Tackle cutting-edge, complex problems such as predicting the optimal location for new Amazon stores by bringing together numerous data assets, and using best-in-class modeling solutions to extract the most information out of them. Work with business stakeholders, software development engineers, and other data scientists across multiple teams to develop innovative solutions at massive is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Bellevue
How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you measure sellers' response to /causal impacts of capacity control policies we implemented at Amazon using the state-of-the-art econometric techniques? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? How do you design and evaluate seller assistance to drive their success? If these type of questions get your mind racing, we want to hear from you.Supply Chain Optimization Technologies (SCOT) optimizes Amazon’s global supply chain end to end and build systems to deliver billions of products to our customers’ doorsteps faster every year while saving hundreds of millions of dollars using economics, operational research, machine learning, and scalable distributed software on the Cloud. Fulfillment by Amazon (FBA) is an Amazon service for our marketplace third party sellers, where our sellers leverage our world-class facilities and provide customers Prime delivery promise on all their goods.We are looking for the next outstanding economist to join our interdisciplinary team of data scientists, research scientists, applied scientists, economists. The ideal candidate combines econometric acumen with strong business judgment. You have versatile modeling skills and are comfortable extracting insights from observational and experimental data. You translate insights into action through proofs-of-concept and partnerships with engineers and data scientists to productionize. You are excited to learn from and alongside seasoned analysts, scientists, engineers, and business leaders. You are an excellent communicator and effectively translate business ideas and technical findings into business action (and customer delight).Key job responsibilitiesProvide data-driven guidance and recommendations on strategic questions facing the FBA leadershipDesign and implement V0 models and experiments to kickstart new initiatives, thinking, and drive system-level changes across AmazonHelp build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challengesInfluence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science!The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal.We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit.The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders).About the teamWe are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
Job summaryThe Global Supply Chain-ACES organization aims to raise the bar on Amazon’s customer experience by delivering holistic solutions for Global Customer Fulfillment that facilitate the effective and efficient movement of product through our supply chain. We develop strategies, processes, material handling and technology solutions, reporting and other mechanisms, which are simple, technology enabled, globally scalable, and locally relevant. We achieve this through cross-functional partnerships, listening to the needs of our customers and prioritizing initiatives to deliver maximum impact across the value chain. Within the organization, our Quality team balances tactical operation with operations partners with global engagement on programs to deliver improved inventory accuracy in our network. The organization is looking for an experienced Principal Research Scientist to partner with senior leadership to develop long term strategic solutions. As a Principal Scientist, they will lead critical initiatives for Global Supply Chain, leveraging complex data analysis and visualization to:a. Collaborate with business teams to define data requirements and processes;b. Automate data pipelines;c. Design, develop, and maintain scalable (automated) reports and dashboards that track progress towards plans;d. Define, track and report program success metrics.e. Serve as a technical science lead on our most demanding, cross-functional projects.