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

IL, Haifa
We’re looking for a Principal Applied Scientist in the Personalization team with experience in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problem Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
DE, Aachen
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Are you a brilliant mind seeking to push the boundaries of what's possible with intelligent robotics? Join our elite team of researchers and engineers - led by Pieter Abeel, Rocky Duan, and Peter Chen - at the forefront of applied science, where we're harnessing the latest advancements in large language models (LLMs) and generative AI to reshape the world of robotics and unlock new realms of innovation. As an Applied Science Intern, you'll have the unique opportunity to work alongside world-renowned experts, gaining invaluable hands-on experience with cutting-edge robotics technologies. You'll dive deep into exciting research projects at the intersection of AI and robotics. This internship is not just about executing tasks – it's about being a driving force behind groundbreaking discoveries. You'll collaborate with cross-functional teams, leveraging your expertise in areas such as deep learning, reinforcement learning, computer vision, and motion planning to tackle real-world problems and deliver impactful solutions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied robotics and AI, where your contributions will shape the future of intelligent systems and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available in San Francisco, CA and Seattle, WA. The ideal candidate should possess: - Strong background in machine learning, deep learning, and/or robotics - Publication record at science conferences such as NeurIPS, CVPR, ICRA, RSS, CoRL, and ICLR. - Experience in areas such as multimodal LLMs, world models, image/video tokenization, real2Sim/Sim2real transfer, bimanual manipulation, open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, and end-to-end vision-language-action models. - Proficiency in Python, Experience with PyTorch or JAX - Excellent problem-solving skills, attention to detail, and the ability to work collaboratively in a team Join us at the forefront of applied robotics and AI, and be a part of the team that's reshaping the future of intelligent systems. Apply now and embark on an extraordinary journey of discovery and innovation! Key job responsibilities - Develop novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of LLMs and generative AI for robotics - Tackle challenging, groundbreaking research problems on production-scale data, with a focus on robotic perception, manipulation, and control - Collaborate with cross-functional teams to solve complex business problems, leveraging your expertise in areas such as deep learning, reinforcement learning, computer vision, and motion planning - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment, and communicate effectively with diverse stakeholders
US, WA, Seattle
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers like Pieter Abbeel, Rocky Duan, and Peter Chen to lead key initiatives in robotic intelligence. As a Senior Applied Scientist, you'll spearhead the development of breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, scence understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between cutting-edge research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for specific research initiatives, ensuring robust performance in production environments - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus A day in the life - Develop and implement novel foundation model architectures, working hands-on with our extensive compute infrastructure - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to efficient multi-task learning - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster - Mentor team members while maintaining significant hands-on contribution to technical solutions Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team, led by pioneering AI researchers Pieter Abbeel, Rocky Duan, and Peter Chen, is building the future of intelligent robotics through groundbreaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
US, WA, Seattle
The Private Brands Discovery team designs innovative machine learning solutions to drive customer awareness for Amazon’s own brands and help customers discover products they love. Private Brands Discovery is an interdisciplinary team of Scientists and Engineers, who incubate and build disruptive solutions using cutting-edge technology to solve some of the toughest science problems at Amazon. To this end, the team employs methods from Natural Language Processing, Deep learning, multi-armed bandits and reinforcement learning, Bayesian Optimization, causal and statistical inference, and econometrics to drive discovery across the customer journey. Our solutions are crucial for the success of Amazon’s own brands and serve as a beacon for discovery solutions across Amazon. This is a high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and engineers. As a scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.. With a focus on bias for action, this individual will be able to work equally well with Science, Engineering, Economics and business teams. Key job responsibilities - 5+ yrs of relevant, broad research experience after PhD degree or equivalent. - Advanced expertise and knowledge of applying observational causal interference methods - Strong background in statistics methodology, applications to business problems, and/or big data. - Ability to work in a fast-paced business environment. - Strong research track record. - Effective verbal and written communications skills with both economists and non-economist audiences.
US, WA, Seattle
The AWS Marketplace & Partner Services Science team is hiring an Applied Scientist to develop science products that support AWS initiatives to grow AWS Partners. The team is seeking candidates with strong background in machine learning and engineering, creativity, curiosity, and great business judgment. As an applied scientist on the team, you will work on targeting and lead prioritization related AI/ML products, recommendation systems, and deliver them into the production ecosystem. You are comfortable with ambiguity and have a deep understanding of ML algorithms and an analytical mindset. You are capable of summarizing complex data and models through clear visual and written explanations. You thrive in a collaborative environment and are passionate about learning. Key job responsibilities - Work with scientists, product managers and engineers to deliver high-quality science products - Experiment with large amounts of data to deliver the best possible science solutions - Design, build, and deploy innovative ML solutions to impact AWS Co-Sell initiatives About the team The AWS Marketplace & Partner Services team is the center of Analytics, Insights, and Science supporting the AWS Specialist Partner Organization on its mission to provide customers with an outstanding experience while working with AWS partners. The Science team supports science models and recommendation systems that are deployed directly to AWS Customers, AWS partners, and internal AWS Sellers.
US, WA, Seattle
The People eXperience and Technology (PXT) Central Science Team uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms, process improvements and products, which simultaneously improve Amazon and the lives, wellbeing, and the value of work of Amazonians. We are an interdisciplinary team which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We invest in innovation and rapid prototyping of scientific models, AI/ML technologies and software solutions to accelerate informed, accurate, and reliable decision backed by science and data. As a research scientist you will you will design and carry out surveys to address business questions; analyze survey and other forms of data with regression models; perform weighting and multiple imputation to reduce bias due to nonresponse. You will conduct methodological and statistical research to understand the quality of survey data. You will work with economists, engineers, and computer scientists to select samples, draft and test survey questions, calculate nonresponse adjusted weights, and estimate regression models on large scale data. You will evaluate, diagnose, understand, and surface drivers and moderators for key research streams, including (but are not limited to) attrition, engagement, productivity, inclusion, and Amazon culture. Key job responsibilities Help to design and execute a scalable global content development and validation strategy to drive more effective decisions and improve the employee experience across all of Amazon Conduct psychometric and econometric analyses to evaluate integrity and practical application of survey questions and data Identify and execute research streams to evaluate how to mitigate or remove sources of measurement error Partner closely and drive effective collaborations across multi-disciplinary research and product teams Manage full life cycle of large-scale research programs (Develop strategy, gather requirements, manage and execute)
US, WA, Seattle
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities - Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). - Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs. - Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions. About the team It's an exciting time to be a leader in AI research. In Amazon's AGI Information team, you can make your mark by improving information-driven experiences of Amazon customers worldwide. Your work will directly impact our customers in the form of products and services that make use of language and multimodal technology!
US, WA, Seattle
Are you excited about developing foundation models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for collaborative scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking an experienced and senior Applied Scientist to focus on computer vision machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
US, CA, East Palo Alto
The Customer Engagement Technology team leads AI/LLM-driven customer experience transformation using task-oriented dialogue systems. We develop multi-modal, multi-turn, goal-oriented dialog systems that can handle customer issues at Amazon scale across multiple languages. These systems are designed to adapt to changing company policies and invoke correct APIs to automate solutions to customer problems. Additionally, we enhance associate productivity through response/action recommendation, summarization to capture conversation context succinctly, retrieving precise information from documents to provide useful information to the agent, and machine translation to facilitate smoother conversations when the customer and agent speak different languages. Key job responsibilities Research and development of LLM-based chatbots and conversational AI systems for customer service applications. Design and implement state-of-the-art NLP and ML models for tasks such as language understanding, dialogue management, and response generation. Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to integrate LLM-based solutions into Amazon's customer service platforms. 4. Develop and implement strategies for data collection, annotation, and model training to ensure high-quality and robust performance of the chatbots. Conduct experiments and evaluations to measure the performance of the developed models and systems, and identify areas for improvement. Stay up-to-date with the latest advancements in NLP, LLMs, and conversational AI, and explore opportunities to incorporate new techniques and technologies into Amazon's customer service solutions. Collaborate with internal and external research communities, participate in conferences and publications, and contribute to the advancement of the field. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!