Amazon's Tal Rabin wins Dijkstra Prize in Distributed Computing

Prize honors Amazon senior principal scientist and Penn professor for a protocol that achieves a theoretical limit on information-theoretic secure multiparty computation.

Secure multiparty computation (MPC) is a computing paradigm in which multiple parties compute an aggregate function — say, their average salary — without revealing any private information — say, their individual salaries — to each other. It’s found applications in auction design, cryptography, data analytics, digital-wallet security, and blockchain computation, among other things.

Tal Rabin.jpeg
Tal Rabin, a senior principal scientist in Amazon Web Services’ cryptography group, a professor of computer science at the University of Pennsylvania, and one of the recipients of the Association for Computing Machinery’s 2023 Dijkstra Prize in Distributed Computing.

In 2023, the Association for Computing Machinery’s annual Dijkstra Prize in Distributed Computing was awarded to three papers on secure MPC from the late 1980s. One of those papers, “Verifiable secret sharing and multiparty protocols with honest majority”, grew out of the doctoral dissertation of Tal Rabin, a senior principal scientist in Amazon Web Services’ cryptography group and a professor of computer science at the University of Pennsylvania. She’s joined on the paper by her thesis advisor, Michael Ben-Or, a professor of computer science at the Hebrew University of Jerusalem, where Rabin earned her PhD.

In a remarkable twist, Rabin’s father, Michael Rabin, also won the Dijkstra Prize, in 2015, making the Rabins the only parent-child pair to have received the award. Even more remarkably, Michael Rabin’s co-recipient was one of his PhD students — Michael Ben-Or.

“So I am my father’s academic grandchild,” Rabin says.

Information-theoretic security

The field of secure MPC got off the ground in 1982, when Andrew Yao, now a professor of computer science at Tsinghua University, published a paper on secure two-party computation. The security of Yao’s MPC scheme, however, depended on the difficulty of factoring large integers — the same computational assumption that ensures the security of most online financial transactions today. Yao’s results immediately raised the question of whether secure MPC was possible even if an adversary had unbounded computational resources, a setting known as the information-theoretic (as opposed to computational) security setting.

Related content
Both secure multiparty computation and differential privacy protect the privacy of data used in computation, but each has advantages in different contexts.

The three 2023 recipients of the Dijkstra Prize all address the problem of information-theoretic secure MPC. The first two papers, both published at the 1988 ACM Symposium on Theory of Computing (STOC), prove that information-theoretic secure MPC is possible if no more than one-third of the participants in the computation are bad-faith actors who secretly share information and collusively manipulate their results.

Tal Rabin and Michael Ben-Or’s paper, which appeared at STOC the following year, improves that ratio to (approximately) one-half, which is provably the maximum number of defectors that can be tolerated in the information-theoretic setting. It’s also the threshold that Yao proved for his original computationally bounded approach.

Today, 35 years after Rabin and Ben-Or’s paper, techniques for information-theoretic secure MPC are beginning to find application. And as general-purpose quantum computers, which can efficiently factor large numbers, inch toward reality, information-theoretic — rather than computational — cryptographic methods become more urgent.

“The goal of our team is to apply MPC techniques to improve security and privacy at Amazon,” Rabin says.

Information checking

The heart of Rabin and Ben-Or’s paper is the adaptation of the concept of a digital signature to the information-theoretic setting. A digital signature is an application of public-key cryptography: The originator of a document has a private signing key and a public verification key, both derived from the prime factors of a very large number. Computing a document’s signature requires the private key, but verifying it requires only the public key. And an adversary can’t falsify the signature without computing the number’s factors.

Rabin and Ben-Or propose a method that they call information checking, which isn’t as powerful as digital signatures but makes no assumptions about defectors’ computational limitations. And it turns out to be an adequate basis for secure multiparty computation.

Related content
Technique that mixes public and private training data can meet differential-privacy criteria while cutting error increase by 60%-70%.

Rabin and Ben-Or’s protocol involves a dealer, an intermediary, and a recipient. The dealer has some data item, s, which it passes to the intermediary, who, at a later time, may in turn pass it to the recipient.

To mimic the security guarantees of digital signatures, information checking must meet two criteria: (1) if the dealer and recipient are honest, the recipient will always accept s if it is legitimate and will, with high probability, reject any fraudulent substitutions; and (2) whether or not the dealer is honest, the intermediary can predict with high probability whether or not the recipient will accept s. Together, these two criteria establish that fraudulent substitutions can be detected if either the dealer or the intermediary (but not both) is dishonest.

To meet the first criterion, the dealer sends the intermediary two values, s and a second number, y. It sends the recipient a different random number pair, (b, c), which satisfy an arithmetic operation (say, y = bs + c). The intermediary knows y and s but neither c nor b; if it attempts to pass the receiver a false s, the arithmetic operation will fail.

Zero-knowledge proofs

To meet the second criterion, Rabin and Ben-Or used a zero-knowledge proof, a mechanism that enables a party to prove that it knows some value without disclosing the value itself. Instead of applying an arithmetic operation to s and a single set of randomly generated numbers, the dealer applies it to s and multiple sets of randomly generated numbers, producing a number of (bi, ci) pairs. After the dealer has sent those pairs to the recipient, the intermediary selects half of them at random and asks the recipient to disclose them.

Since the intermediary knows s, it can determine whether the arithmetic relationship holds and, thus, whether the dealer has sent the recipient valid (bi, ci) pairs. On the other hand, since the intermediary doesn’t know the undisclosed pairs, it can’t, if it’s dishonest, game the system by trying to pass the recipient false y’s along with false s’s.

Secure multiparty computation.gif
A sample implementation of the zero-knowledge proof that Tal Rabin and her coauthor, Michael Ben-Or, used to establish that the intermediary in their multiparty-computation protocol could detect attempts by the dealer to cheat.

From weak to verifiable secret sharing

Next, Rabin and Ben-Or generalize this result to the situation in which there are multiple recipients, each receiving its own si. In this context, the authors show that their protocol enables weak secret sharing, meaning that if the recipients are trying to collectively reconstruct a value from their respective si’s, either they’ll reconstruct the correct value, or the computation will fail.

Providing a basis for secure MPC, however, requires the stronger standard of verifiable secret sharing, meaning that no matter the interference, the recipients’ collective reconstruction will succeed. The second major contribution made by Rabin and Ben-Or’s paper is a method for leveraging weak secret sharing to enable verifiable secret sharing.

Related content
Amazon is helping develop standards for post-quantum cryptography and deploying promising technologies for customers to experiment with.

In Rabin and Ben-Or’s protocol, all the (bi, ci) pairs sent to all the recipients are generated using the same polynomial function. In the multiple-recipient setting, the degree of the polynomial — its largest exponent — is half the number of recipients. To establish that a secret has been correctly shared, the dealer needs to show that all the received pairs fit the polynomial — without disclosing the polynomial itself. Again, the mechanism is a zero-knowledge proof.

“What we want is for parties to commit to their values via the weak secret sharing,” Rabin explains. “So now you know it's either one value or nothing. And then the dealer, on these values, proves that they all sit on a polynomial of degree T. Once that proof is done, you know about the values shared with weak secret sharing that they'll either be opened or not opened. You know that everything that is opened is on the same polynomial of degree T. And now you know you can reconstruct.”

When Rabin and Ben-Or published their paper, MPC research was in its infancy. “You can do information checking much better, much more efficiently and so on, today,” Rabin says. But the paper’s central result was theoretical. Today, designers of secure-MPC protocols can use any proof mechanism they choose, and they’ll enjoy the same guarantees on computability and defection tolerance that Rabin and Ben-Or established 35 years ago.

Related content

US, MA, Boston
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 technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support 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 generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior 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 generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.