Cryptographic computing can accelerate the adoption of cloud computing

Amazon Scholar Joan Feigenbaum talks about two cryptographic techniques that are being used to address cloud-computing privacy concerns and accelerate enterprise cloud adoption.

  1. Joan Feigenbaum
    Joan Feigenbaum, Amazon Scholar

    Joan Feigenbaum is an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale. In this article, Feigenbaum talks about secure multiparty computation (MPC) and privacy-preserving machine learning (PPML) – two cryptographic techniques that are being used to address cloud-computing privacy concerns and accelerate enterprise cloud adoption.

    According to a 2019 report released by Cybersecurity Insiders, security risks—including the loss or leakage of information—are leading factors that discourage enterprises and government organizations from adopting cloud-computing technologies. As organizations accelerate the flow of sensitive consumer information to the cloud in order to take advantage of its massive compute power, the research area of cryptographic computing is growing in importance.

    At its essence, cryptographic computing focuses on the design and implementation of protocols for using information without revealing it. For example, a county government looking to prioritize the rollout of services based on different areas’ demographics could calculate the average age of residents in different zip codes without running the risk of revealing (indeed without even learning) the ages of individual residents.

    Cryptographic computing is not a new field. In fact, Gentry’s breakthrough scheme for fully homomorphic encryption (FHE) was published as far back as 2008.

    In one of its extensively studied forms, FHE gives each user a public key and a corresponding private key. A user can encrypt any input data set using the public key, give the encrypted input to another party (say a cloud-computing service) that performs computations on it, and then decrypt the results of those computations with her secret key. By ensuring that all data are operated on only in an encrypted state, FHE ensures that data uploaded to the cloud remain confidential. Unfortunately, FHE is not yet fast enough for use on very large-scale data sets.

    That said, there are more narrowly tailored cryptographic-computing techniques that scale better and have started to see commercial use.

  2. Secure multi-party computation (MPC)

    Secure multi-party computation (MPC) enables n parties P1,...,Pn, with private inputs x1,...,xn, to compute y = f(x1,...,xn) in such a way that all parties learn y but no Pi learns anything about xj, for ji, except what is logically implied by y and xi.

    Consider the following toy example. Suppose 20 pupils, whom we will call P1 through P20, are in the same class and have received their graded exams from their teacher. They want to compute the average of their grades without revealing their individual grades, which we will denote by g1 through g20. They can use the following simple MPC protocol. P1 chooses a random number r, computes x1 = g1 + r, and sends x1 to P2. Then P2 computes x2 = x1 + g2 and sends x2 to P3. They continue in this fashion until P20 computes x20 = x19 + g20 and sends x20 to P1. In the last step, P1 computes x20 – r, which is of course the sum g1 + g2 + … + g20 of the individual grades. He divides this sum by 20 to obtain the average and broadcasts the result to all of the pupils.

    If all of the pupils follow this protocol faithfully, then they all learn the average, but none learns anything about the others’ grades except what is logically implied by the average and his own grade. Here, “following the protocol faithfully” requires not colluding with another pupil to discover someone else’s grade. If, say, P3 and P5 executed all of the steps of the protocol correctly but also got together on the side to pool their information, they could compute P4’s grade g4. That is because g4 = x4 – x3, and, during the execution of the protocol, P3 learns x3 and P5 learns x4. Fortunately, there are techniques (the details of which are beyond the scope of this article) for ensuring that this type of collusion does not reveal private inputs; they include secret-sharing schemes, described below.

    One powerful class of MPC protocols proceeds in multiple rounds. In the first round, each Pi breaks xi into shares, using a secret-sharing scheme, and sends one share to each Pj. The information-theoretic properties of secret sharing guarantee that no other party (or even limited-sized coalition of other parties) can compute xi from the share(s). The parties then execute a multi-round protocol to compute shares of y, in which the shares of intermediate results computed in each round also do not reveal xi. In the last round, the parties broadcast their shares of y so that all of them can reconstruct the result.

    In the secure-outsourcing protocol architecture, depicted below, the parties P1,...,Pn play the role of input providers and a disjoint, much smaller set of parties S1,...,Sk play the role of secure-computation servers; typically, 2 ≤ k ≤ 4. The input providers share their inputs with the servers, which then execute a basic, k-party MPC protocol to compute y. For an appropriate choice of secret-sharing scheme, the inputs remain private as long as at least one server does not collude with the others. Note that cloud-computing companies are ideally positioned to supply secure computation servers!

    MPC.JPG
    The Secure-Outsourcing Architecture with n=8 and k=4
    Image credit: Joan Feigenbaum

  3. Privacy-preserving machine learning (PPML)

    An ML training algorithm is given a set of solved instances of a classification problem and produces a model to be used by an ML prediction algorithm to classify future, as-yet-unsolved instances of the same problem.

    Training data, queries (inputs to the prediction algorithm), and predictions (outputs of the prediction algorithm) may contain sensitive information about data subjects. Owners of commercially valuable models regard them as intellectual property and may wish to sell access to them but not permit users to reverse-engineer them. Privacy-preserving machine learning (PPML) is the subarea of cryptographic computing that studies algorithms that protect training data, models, queries, and predictions.

    Practical PPML methods are often tailored for specific training or prediction algorithms and may require specific computational architectures. The cloud provider can employ both traditional computer-security techniques (authentication, sandboxing, etc.) and PPML algorithms to protect both sensitive data and intellectual property. For example, the 2019 PPML annual workshop focused on MPC, FHE, and other techniques outlined in this article. In addition, the workshop featured recent results on differential privacy, a powerful data-protection approach that has gained a lot of attention in recent years. Differential privacy enables users to obtain aggregate information from a database while protecting confidential information about individual records in the database. Indeed, the result of a differentially private statistical query is not significantly affected by the presence or absence of any particular individual record.

    PPMLSchema.JPG
    Image credit: Joan Feigenbaum and Xianrui Meng

    Secure, multi-party computation and privacy-preserving machine learning are only two cryptographic-computing techniques that are candidates for widespread practical deployment. Other techniques include searchable encryption, which enables keyword search on encrypted documents, garbled-circuit protocols, which are a form of secure, two-party computation, and protocols for queries to encrypted databases.

    I’m personally excited to see these innovations in cryptographic computing, which will be critical to easing contractual and regulatory barriers to adoption of cloud computing and could herald an era of even stronger growth for the industry. Cryptographic computing will allow individuals around the globe to reap the benefits of cloud computing, such as personalized medicine, movie streaming, and smarter financial-management solutions, while ensuring that our personal information stays private and secure.


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Job summaryInteresting ML and causal inference problems, high visibility, cross-team collaborations with other scientists and engineersKey job responsibilitiesAmazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment.Applied Scientists at Amazon will be expected to work directly with senior management on key business problems faced in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon Applied Scientists will apply the frontier of Machine Learning to forecasting, detection, online advertising and other areas. You will build machine learning science models, using our world class data systems, and apply machine learning techniques to solve business problems in a fast moving environment. Applied Scientists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.At Customer Trust and Partner Support, we aim to make Amazon the safest place for our Customers, Selling Partners, and Brands, and the best place to build thriving business for our Selling Partners and Brands.As an applied scientist on our team, your role is to leverage your strong background in Computer Science and Machine Learning to help build the next generation of our model development and assessment pipeline, harness and explain rich data at Amazon scale, and provide automated insights to improve machine learned solution that impact millions of customers and sellers every day. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations.A day in the lifedeveloping modelcode reviewcommunicating results to biz stakeholderAbout the teamPOE Econ is a center of excellence for providing analytical solutions that marry ML and causal inference techniques at scale to measure long-term impacts to the teams and enable impact-based decision making.
US, WA, Seattle
Job summaryIn Amazon Advertising we are applying Machine Learning techniques at massive scale to optimize advertising performance. We are looking for talented applied scientists to perform research, prototyping, and experimentation with the latest ML techniques. In this role you will work together with other Applied Scientists and Data Scientists, Software and Data Engineers, and Product Managers on high impact initiatives in Amazon’s Multi Channel Advertising.The Ad Performance and Delivery team owns the core systems for optimizing Ad Selection, Bidding, and Campaign Performance. These systems process billions of Ad impressions daily from across the internet to power our display advertising algorithms. You will play a significant role in building and improving ML-based optimization systems for Amazon’s display, audio, and video advertising. You will research the latest advances in ML prediction and forecasting techniques, construct prototypes to validate your hypothesis, work with engineers to turn these prototypes into production systems, and perform offline analysis and online A/B experimentation.In this role, you will work directly with a highly motivated team of ML scientists and engineers who are passionate about applying data science and ML techniques to solve advertiser needs. You will also join the lively Amazon ML community, with opportunities to write papers, attend workshops and conferences (internal and external), participate in science events, and obtain early access to Amazon ML technologies.As a Senior Applied Scientist on this team, you will:· Be a technical leader in Machine Learning and drive full life-cycle Machine Learning projects.· Lead technical efforts within this team and across other teams.· Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.· Run A/B 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.· Work closely with software engineers to assist in productionizing your ML models.· Research new and innovative machine learning approaches.· Recruit Applied Scientists to the team and mentor scientists on the team.Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US
Job summaryWant to help the largest global enterprises derive business value through the adoption of Artificial Intelligence (AI) and Machine Learning (ML)? Excited by using massive amounts of disparate data to develop ML models? Eager to learn to apply ML to a diverse array of enterprise use cases? Thrilled to be a part of Amazon who has been pioneering and shaping the world’s AI/ML technology for decades?At Amazon Web Services (AWS), we are helping large enterprises build ML models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. AWS Professional Services works together with AWS customers to address their business needs using AI solutions.AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions.Major responsibilities include:· Assist customers by being able to deliver a ML project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining to deliver business impact to the organization· Use AWS AI services (e.g., Personalize), ML platforms (SageMaker), and frameworks (e.g., MXNet, TensorFlow, PyTorch, SparkML, scikit-learn) to help our customers build ML models· Research and implement novel ML approaches, including hardware optimizations on platforms such as AWS Inferentia· Work with our other Professional Services consultants (Big Data, IoT, HPC) to analyze, extract, normalize, and label relevant data, and with our Professional Services engineers to operationalize customers’ models after they are prototypedInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.This is a customer-facing role and you will be required to travel to client locations and deliver professional services as needed.
US
Job summaryThe AWS Intelligent and Advanced Computing Technologies (IACT) organization is growing an application development team.Our organization specializes in solving elusive problems. We are a hybrid team of machine learning, quantum computing and high performance computing professionals who directly support AWS customers with custom and off-the-shelf applications.Our team offers the opportunity to engage directly with advanced technologies projects (e.g. computer vision, natural language processing, quantum simulation, and optimization) while supporting AWS customers (e.g. startups, Fortune 500, nonprofit, etc.) across all industries.Key job responsibilitiesYou should be ready to collaborate with customer and design teams to support machine learning application development from a security perspective. This includes analyzing AWS application operation at the API level and ensuring that applications exceed security expectations. You should feel empowered to collaborate through ambiguous customer environments and understand how IACT applications can best solve customer needs.Within AWS, you will work closely with Machine Learning, Quantum Computing, and High Performance Computing teams both within Professional Services and externally. You can expect to work with customer product managers, architects, and executives (e.g. Dir., VP, CxO).About the teamInclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.Work/Life BalanceOur team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.
US, CA, Irvine
Job summaryAs the Applied Science Manager within Personalization, you will help build a team that leads creation and innovation on the next generation of Recommendation and Personalization technologies on Amazon. Our team owns creating recommendation solutions that understand long-term customer shopping patterns, Spanning Academia, research, and applied machine learning techniques operating at world-class scale, we focus on recommending the right product to the right customer at the right time. You will be surrounded by thought-leaders in the Personalization space who are patent-leaders within Amazon.In Personalization we use state-of-the-art machine learning techniques and A/B testing to run experiments on some of Amazon’s most prominent and valuable pages. We work on a diverse range of products, building real-time, low-latency recommendation and ranking systems as well as building algorithms for understanding customer behavior and generating recommendations content. You will hone your skills in areas such as deep learning and collaborative filtering while building scalable industrial machine learning systems that handle millions of requests a day. You will lead a collaborative team of experienced engineers. You will have a unique opportunity to drive direct, measurable impact to our customers, powering features on Amazon.About the team: Our team has the autonomy to decide where we can have the most impact and get down to experimenting. We love metrics and the fast pace. We analyze data to uncover potential opportunities, generate hypotheses, and test them. We refuse to accept constraints, internal or external, and have a strong bias for action. We imagine, build prototypes, validate ideas, and launch follow-up experiments from the successful ones.About you: You are an entrepreneurial Applied Science Manager with an interest in machine learning, data science, search, or recommendation systems. You love keeping abreast of the latest technology and use it to help you innovate. You have strong leadership qualities, great judgment, clear communication skills, and a track record of delivering great products and hiring/coaching successful teams. You enjoy working hard, having fun, and making history!
US, CA, Cupertino
The Team: Amazon Go is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go!Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your Amazon account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design.The Role: Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems.As a Computer Vision Research Scientist, you will help solve a variety of technical challenges and mentor other engineers. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people.
IN, KA, Bangalore
Job summaryThe Team: Amazon One is a fast, convenient, contactless way for people to use their palm to make everyday activities like paying at a store, presenting a loyalty card, entering a location like a stadium, or badging into work more effortless. The service is designed to be highly secure and uses custom-built algorithms and hardware to create a person’s unique palm signature. Designed and custom-built by Amazonians, it uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design.The Role: Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems.If you have expertise leading Computer Vision research teams and have a Ph.D, or an Masters and 2+ years of industry experience and have: - the ability to recognize and champion new ideas and novel solutions;· the insight to correctly identify paths worth exploring;· the discipline to fast-fail when data refutes theory;· and the fortitude to continue exploring until your solution is foundCome join us invent the future and change the world.
US, CA, Sunnyvale
Job summaryAmong the goals of the Alexa AI team is to make Alexa the most knowledgeable and trusted source of local information to enable customers to find places, products, or services around them; ask detailed questions about points of interests; and to provide personalized travel, commute, and navigation experiences - while in the car, on the go, or at home.We are seeking a Data Scientist to be part of the NLU Science team for Alexa Local Information. This is a strategic role to shape and deliver our technical strategy in developing and deploying natural language understanding and machine learning solutions to our hardest customer-facing problems. This role requires collaborating closely with business, engineering and other scientists within Local Information and across the Alexa organization. You will work with a team of Language Engineers and Scientists to launch new customer facing features and improve the current experiences.Key job responsibilities· Apply machine learning and SLU techniques to improve our conversational interfaces and search algorithms· Collaborate with Scientists and Language Engineers in developing coherent and comprehensive meaning representation frameworks· Perform hands-on data analysis and modeling with large data sets to develop insights that increase device usage and customer experience· Run A/B experiments, evaluate the impact of your optimizations and communicate your results to various business stakeholders· Work closely with product managers and software engineers to design experiments and implement end-to-end solutions· Set up and monitor alarms to detect anomalous data patterns and perform root cause analyses to explain and address them· Handle competing requests from a range of data customers· Be a member of the Amazon-wide machine learning community, participating in internal and external Meet-Ups, Hackathons and Conferences· Help attract and recruit technical talent
US, WA, Seattle
Job summaryAmazon Web Services (AWS) is obsessed with ensuring the success of our customers. We want to nurture many new customers through use of technical support, credits, and training programs. To this end, we need to understand what types of customers respond the most to these different benefits. Can we tell who needs help from the sales force vs. those who are more motivated by sticker price? Do the impacts of credits or sales help vary by customer tenure, or industry? These are the types of questions we'll answer to help drive the growth of AWS.Job Locations:· Seattle, WA· Dallas, TX· Arlington, VA· Boston, MA· Vancouver, CAKey job responsibilitiesBeyond the usual technical skills, ownership, customer obsessions, and deliver results are key for this team. We need people who can understand customer needs, build a prototype, collect feedback from the customer, build some more, lather, rinse and repeat. Our team contains a wide diversity of tech talent and we can get you help when you're blocked. But we do need you to understand your customers' requirements and work backwards.About the teamWe're a team of economists, data and applied scientists, and engineers who work on projects to help make the best use of AWS's thousands of talented sales representatives. We have great data support from our sister teams and have more than tripled our team size by thinking big and delivering. We'd love to chat with you and answer any questions you have!
US, WA, Redmond
Job summaryAs an Applied Scientist on the team you will responsible for building out and maintaining the algorithms and software services behind one of the world’s largest satellite constellations. You will be responsible for developing algorithms and applications that provide mission critical information derived from past and predicted satellite orbits to other systems and organizations rapidly, reliably, and at scale.You will be focused on contributing to the design and analysis of software systems responsible across a broad range of areas required for automated management of the Kuiper constellation. You will apply knowledge of mathematical modeling, optimization algorithms, astrodynamics, state estimation, space systems, and software engineering across a wide variety of problems to enable space operations at an unprecedented scale. You will develop features for systems to interface with internal and external teams, predict and plan communication opportunities, manage satellite orbits determination and prediction systems, develop analysis and infrastructure to monitor and support systems performance. Your work will interface with various subsystems within Project Kuiper and Amazon, as well as with external organizations, to enable engineers to safely and efficiently manage the satellite constellation.The ideal candidate will be detail oriented, strong organizational skills, able to work independently, juggle multiple tasks at once, and maintain professionalism under pressure. You should have proven knowledge of mathematical modeling and optimization along with strong software engineering skills. You should be able to independently understand customer requirements, and use data-driven approaches to identify possible solutions, select the best approach, and deliver high-quality applications.Export Control Requirement:Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.About the teamThe Constellation Management & Space Safety team maintains build the software services responsible for maintaining situational awareness for Kuiper satellites and coordinating with internal and external organizations to maintain the nominal operational state. We build automated systems that use satellite telemetry and other related data to predict future orbits, plan maneuvers to avoid high risk conjunctions, and keep satellites in the desired locations. Using knowledge of software engineering and space systems, we provide visibility information that is used to predict and establish communication channels for Kuiper satellites.