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November 30, 2022

Tools for designing some exciting chips

Borivoje Nikolić
University of California Berkeley
10:00A
- 11:00A

Location

36-462 (Allen Room)
Add to Calendar 2022-11-30 10:00:00 2022-11-30 11:00:00 America/New_York Tools for designing some exciting chips Zoom: https://mit.zoom.us/j/97274768449ABSTRACT: There is an enormous interest in developing customized, domain-specific systems-on-a-chip. Continued improvement in computing efficiency requires functional specialization of hardware designs. But designing complex chips is difficult. This talk presents the Chipyard framework, an integrated SoC design, simulation, and implementation environment for specialized compute systems. Chipyard includes configurable, composable, open-source, generator-based IP blocks that can be used across multiple stages of the hardware development flow while maintaining design intent and integration consistency. We discuss some sample designs, the use of the framework in classes, and opportunities for extension and improvement.BIO: Borivoje Nikolić is a Professor of Electrical Engineering and Computer Sciences at the University of California at Berkeley, where he holds the National Semiconductor Distinguished Professorship in Engineering. He co-directs the Specialized Computing Ecosystems (SLICE) lab and the Berkeley Wireless Research Center (BWRC).-- 36-462 (Allen Room)

TALK: "Breaking Conventional Wisdom: a Linearly-Scalable Layer-1 Blockchain”

Geoff Ramseyer
Stanford University
1:00P
- 2:30P

Location

32-123
large classroom / auditorium. In-person event, open to CSAIL community.
Add to Calendar 2022-11-30 13:00:00 2022-11-30 14:30:00 America/New_York TALK: "Breaking Conventional Wisdom: a Linearly-Scalable Layer-1 Blockchain” The host, Professor Adam Belay, writes: Hi All, During our graduate-level Operating Systems seminar (6.581), we will have a guest lecture from Geoff Ramseyer of Stanford University on a new, highly scalable, decentralized exchange system called SPEEDEX. This talk will be open to MIT guests outside of the class, and the CSAIL community is welcome to attend.Abstract: SPEEDEX is a decentralized exchange (DEX) that lets participants securely trade assets without giving any single party undue control over the market. SPEEDEX offers several advantages over prior DEXes. It achieves high throughput --- over 200,000 transactions per second on 48-core servers, even with tens of millions of open offers. SPEEDEX runs entirely within a Layer-1 blockchain, and thus achieves its scalability without fragmenting market liquidity between multiple blockchains or rollups. It eliminates internal arbitrage opportunities, so that a direct trade from asset A to B always receives as good a price as trading through some third asset, such as USD. Finally, it prevents certain front-running attacks that would otherwise increase the effective bid-ask spread for small traders.SPEEDEX's key design insight is its use of an Arrow-Debreu exchange market structure that fixes the valuation of assets for all trades in a given block of transactions. We construct an algorithm, which is both asymptotically efficient and empirically practical, that computes these valuations while exactly preserving a DEX's financial correctness constraints. Not only does this market structure provide fairness across trades, but it also makes trade operations commutative and hence efficiently parallelizable. SPEEDEX is prototyped but not yet merged within one of the largest Layer-1 blockchains.Geoff Ramseyer is a final year PhD student at Stanford University advised by David Mazières and Ashish Goel. He is interested broadly in market design and the interplay between market design and system performance. Recent projects include implementing SPEEDEX and a scalable CBDC ledger, with a focus on maximizing hardware parallelism. 32-123

Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures

Aparna Gupte
MIT
4:00P
- 5:00P

Location

32-G575
Add to Calendar 2022-11-30 16:00:00 2022-11-30 17:00:00 America/New_York Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures Abstract: The Learning With Errors (LWE) problem, introduced by Regev in 2005, is the average-case problem of solving a noisy system of random linear equations over a finite ring. Regev showed that LWE is computationally hard assuming certain worst-case lattice problems are hard. Beyond its connections to lattices, LWE has been seminal in cryptography by enabling public key encryption, fully-homomorphic encryption, and lots more. In 2021, Bruna, Regev, Song and Tang introduced a continuous variant of LWE, called Continuous LWE (CLWE), and gave a quantum reduction showing that CLWE is computationally hard assuming some worst-case lattice problems are hard. Moreover, assuming the hardness of CLWE, they showed that learning Gaussian mixtures in n dimensions, with \sqrt{n} Gaussian components, is not possible in polynomial time. Several works have since shown the computational hardness of a number of other statistical learning problems, assuming the hardness of CLWE.In this talk, I will present a direct (classical) reduction from LWE to CLWE, following a series of conceptually simple transformations. This reduction from LWE to CLWE allows us to bring the well-developed machinery of LWE-based cryptography to CLWE and its applications. For example, it allows us to obtain the hardness of CLWE under the classical hardness of worst-case lattice problems. Previously, the hardness of CLWE was known only under the quantum hardness of worst-case lattice problems. Moreover, assuming the polynomial hardness of LWE, this reduction allows us to show the hardness of learning n^\epsilon Gaussian components for any \epsilon > 0, which improves on Bruna et al., who show hardness for at least \sqrt{n} Gaussian components under polynomial (quantum) worst-case hardness assumptions. Under the subexponential hardness of LWE, our reduction implies the hardness of learning roughly \log n Gaussian components. This talk is based on joint work with Neekon Vafa and Vinod Vaikuntanathan, and is available at https://arxiv.org/abs/2204.02550. 32-G575

December 01, 2022

Techniques for Interpretability and Transparency of Black-Box Models

Yilun Zhou
MIT CSAIL
10:00A
- 12:00P

Location

Seminar Room D463 (Star)
Add to Calendar 2022-12-01 10:00:00 2022-12-01 12:00:00 America/New_York Techniques for Interpretability and Transparency of Black-Box Models Abstract: Recently, black-box models, such as neural networks, have been increasingly adopted in many tasks. However, their opacity, or the inability to understand their inner-workings, has hindered the deployment in high-stakes domains such as healthcare or finance. In this talk, I describe my research in interpretability and transparency to address this issue. In the interpretability category, I introduce two fundamental properties of good explanations for model predictions, correctness and understandability. Correctness captures the notion that the explanations should faithfully represent the model’s decision making logic, and understandability reflects the requirement that these explanations should be reliably understood by human users. For both properties, I propose evaluation metrics as well as methods that improve upon existing ones, while identifying avenues for future work. In the transparency category, I present the transparency-by-example framework, a Bayesian sampling formulation to inspect models and identify a wide range of model behaviors. I demonstrate the flexibility of this Bayesian approach by applying it to both deep neural networks and non-differentiable robot controllers, revealing hidden and hard-to-find insights in both cases. Seminar Room D463 (Star)

December 02, 2022

December 05, 2022

MIT Bioinformatics Seminar: Barry Honig: Structure-based proteome wide prediction of protein-protein and protein-compound interactions

Barry Honig, Columbia
11:30A
- 1:00P

Location

Virtual Event
Add to Calendar 2022-12-05 11:30:00 2022-12-05 13:00:00 America/New_York MIT Bioinformatics Seminar: Barry Honig: Structure-based proteome wide prediction of protein-protein and protein-compound interactions ABSTRACT: I will describe our research program involving the prediction of protein-protein interactions (PPIs). Underlying our general approach is the use of 3D-structural similarity to detect functional relationships between proteins. When combined with simple and fast filtering functions we are able to score billions of potential PPIs with relatively moderate computational demands. I will review how this is accomplished with focus on the development and application of our Predicting Protein-Protein Interactions (PrePPI) and Pathogen Host Interactions (P-HIPSTer) databases. Further, PrePPI has been integrated with PPIs inferred from reverse engineering algorithms from patient-sample genomic data to yield structure-informed cell and tumor-specific regulatory networks. Finally, we have used related methodologies to create the Predicting Protein Compound Interactions (PrePCI) database whose features will be described. https://mit.zoom.us/j/92936688687

Aligning Robot Representations with Humans

Andreea Bobu
UC Berkeley
4:00P
- 5:00P

Location

31-270 or Zoom: https://mit.zoom.us/j/93936194315
Add to Calendar 2022-12-05 16:00:00 2022-12-05 17:00:00 America/New_York Aligning Robot Representations with Humans This seminar is part of the New Trends in Aerospace Seminar in the Department of Aeronautics & Astronautics. Zoom: https://mit.zoom.us/j/93936194315Abstract: Robots deployed in the real world will interact with many different humans to perform many different tasks in their lifetime, which makes it difficult (perhaps even impossible) for designers to specify all the aspects that might matter ahead of time. Instead, robots can extract these aspects implicitly when they learn to perform new tasks from their users' input. The challenge is that this often results in representations which pick up on spurious correlations in the data and fail to capture the human’s representation of what matters for the task, resulting in behaviors that do not generalize to new scenarios. Consequently, the representation, or abstraction, of the tasks the human hopes for the robot to perform may be misaligned with what the robot knows. In my work, I explore ways in which robots can align their representations with those of the humans they interact with so that they can more effectively learn from their input. In this talk I focus on a divide and conquer approach to the robot learning problem: explicitly focus human input on teaching robots good representations before using them for learning downstream tasks. We accomplish this by investigating how robots can reason about the uncertainty in their current representation, explicitly query humans for feature-specific feedback to improve it, then use task-specific input to learn behaviors on top of the new representation. Bio: Andreea Bobu is a Ph.D. candidate at the University of California Berkeley in the Electrical Engineering and Computer Science Department advised by Professor Anca Dragan. Her research focuses on aligning robot and human representations for more seamless interaction between them. In particular, Andreea studies how robots can learn more efficiently from human feedback by explicitly focusing on learning good intermediate human-guided representations before using them for task learning. Prior to her Ph.D. she earned her Bachelor’s degree in Computer Science and Engineering from MIT in 2017. She is the recipient of the Apple AI/ML Ph.D. fellowship, is a Rising Star in EECS and an R:SS and HRI Pioneer, has won best paper award at HRI 2020, and has worked at NVIDIA Research. 31-270 or Zoom: https://mit.zoom.us/j/93936194315

December 06, 2022

Film screening & fireside: Patrick and the Whale

Patrick Diykstra

Part Of

4:00P
- 6:00P

Location

32-141
Add to Calendar 2022-12-06 16:00:00 2022-12-06 18:00:00 America/New_York Film screening & fireside: Patrick and the Whale Film Screen & Fireside on 'Patrick & the Whale'join CSAIL Director Daniela Rus for a special Hot Topics in Computing on Tuesday, December 6, 2022 from 4-6pm in 32-141. We will have a screening of the film 'Patrick and the Whale', followed by a fireside conversation with Patrick Dykstra. We will discuss the use of computing for wildlife conservation. Please join us for this unique opportunity!Bio:Patrick Dykstra is the co-star of the semi-biographical feature film ‘Patrick and the Whale’ which chronicle’s Patrick’s decade long connection with a family of sperm whales. The film won the Audience Choice Award at the Innsbruck Film Festival, Newport Beach Film Festival, Graz Film Festival and was nominated for both a Panda award (Wildscreen) and Jackson Wild Media Award as well as the Golden Eye award (Zurich Film Festival).Patrick also hosted and filmed the series Chasing Ocean Giants (Discovery Channel) that has aired in over 150 countries. The eight-part series follows Patrick’s journey across the globe to assist some of the world’s leading scientists in discovering mysteries of the ocean. During the production the team filmed numerous world-firsts and provided a valuable platform for the scientists with whom they worked.Patrick won a BAFTA for his cinematography work on the BBC’s Blue Planet 2 and has since filmed nature programs for Netflix, National Geographic, AppleTV+, Discovery, BBC and others.Prior to his work in natural history, Patrick spent eight years as a corporate lawyer working at one of the world’s most prestigious international law firms representing some of the world’s largest companies and was based in New York, Los Angeles and Dubai before leaving the corporate life behind to pursue his passions.Since leaving the corporate world Patrick has visited 102 countries and has filmed in some of the harshest environments including Yemen’s tribal areas, diving under Antarctic ice and at the top of Congo’s erupting volcanos.He is a certified rebreather scuba diver, wingsuit skydiver, hang-glider and para-glider pilot and is passionate about wildlife conservation. When not on the road Patrick is at home in Bristol, UK. 32-141

Film Screen & Fireside on 'Patrick & the Whale'

Patrick Dykstra

Part Of

4:00P
- 6:00P

Location

32-141
Add to Calendar 2022-12-06 16:00:00 2022-12-06 18:00:00 America/New_York Film Screen & Fireside on 'Patrick & the Whale' join CSAIL Director Daniela Rus for a special Hot Topics in Computing on Tuesday, December 6, 2022 from 4-6pm in 32-141. We will have a screening of the film 'Patrick and the Whale', followed by a fireside conversation with Patrick Dykstra. We will discuss the use of computing for wildlife conservation. Please join us for this unique opportunity! Bio:Patrick Dykstra is the co-star of the semi-biographical feature film ‘Patrick and the Whale’ which chronicle’s Patrick’s decade long connection with a family of sperm whales. The film won the Audience Choice Award at the Innsbruck Film Festival, Newport Beach Film Festival, Graz Film Festival and was nominated for both a Panda award (Wildscreen) and Jackson Wild Media Award as well as the Golden Eye award (Zurich Film Festival). Patrick also hosted and filmed the series Chasing Ocean Giants (Discovery Channel) that has aired in over 150 countries. The eight-part series follows Patrick’s journey across the globe to assist some of the world’s leading scientists in discovering mysteries of the ocean. During the production the team filmed numerous world-firsts and provided a valuable platform for the scientists with whom they worked. Patrick won a BAFTA for his cinematography work on the BBC’s Blue Planet 2 and has since filmed nature programs for Netflix, National Geographic, AppleTV+, Discovery, BBC and others.Prior to his work in natural history, Patrick spent eight years as a corporate lawyer working at one of the world’s most prestigious international law firms representing some of the world’s largest companies and was based in New York, Los Angeles and Dubai before leaving the corporate life behind to pursue his passions.Since leaving the corporate world Patrick has visited 102 countries and has filmed in some of the harshest environments including Yemen’s tribal areas, diving under Antarctic ice and at the top of Congo’s erupting volcanos. He is a certified rebreather scuba diver, wingsuit skydiver, hang-glider and para-glider pilot and is passionate about wildlife conservation. When not on the road Patrick is at home in Bristol, UK.

Juho Kim - Interaction-Centric AI

Juho Kim
KAIST
4:00P
- 5:00P

Location

32-D463 (Star)
Add to Calendar 2022-12-06 16:00:00 2022-12-06 17:00:00 America/New_York Juho Kim - Interaction-Centric AI Abstract:Remarkable model performance makes news headlines and compelling demos, but these advances rarely translate to a lasting impact on real-world users. A common anti-pattern is overlooking the dynamic, complex, and unexpected ways humans interact with AI, which in turn limits the adoption and usage of AI in practical contexts. To address this, I argue that human-AI interaction should be considered a first-class object in designing AI applications.In this talk, I present a few novel interactive systems that use AI to support complex real-life tasks. I discuss tensions and solutions in designing human-AI interaction, and critically reflect on my own research to share hard-earned design lessons. Factors such as accuracy disparity between user groups, user motivation, social dynamics, and sustainable engagement often play a crucial role in determining the user experience of AI, even more so than model accuracy. My call to action is that we need to establish robust building blocks for “Interaction-Centric AI”—a systematic approach to designing and engineering human-AI interaction that complements and overcomes the limitations of model- and data-centric views.Bio:Juho Kim is an Associate Professor in the School of Computing at KAIST, affiliate faculty in the Kim Jaechul Graduate School of AI at KAIST, and a director of KIXLAB (the KAIST Interaction Lab). His research in human-computer interaction and human-AI interaction focuses on building interactive and intelligent systems that support interaction at scale, with the goal of improving the ways people learn, collaborate, discuss, make decisions, and take action online. He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 14 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP. He is currently spending his sabbatical year at Ringle Inc., a startup building an online language tutoring platform, to transfer his research on automatically analyzing and diagnosing learners’ English proficiency into a real product.This seminar will also be streamed over Zoom: https://mit.zoom.us/j/96025429888. 32-D463 (Star)

December 07, 2022

Shading Languages and the Emergence of Programmable Graphics Systems

Pat Hanrahan
Stanford University

Part Of

5:00P
- 6:00P

Location

32-123
Kirsch Auditorium
Add to Calendar 2022-12-07 17:00:00 2022-12-07 18:00:00 America/New_York Shading Languages and the Emergence of Programmable Graphics Systems Title: Shading Languages and the Emergence of Programmable Graphics SystemsAbstract: A major challenge in using computer graphics for movies and games is to create a rendering system that can create realistic pictures of a virtual world.The system must handle the variety and complexity of the shapes, materials, and lighting that combine to create what we see every day. The images must also be free of artifacts, emulate cameras to create depth of field and motion blur, and compose seamlessly with photographs of live action.Pixar's RenderMan was created for this purpose,and has been widely used in feature film production. A key innovation in the system is to use a shading language to procedurally describe appearance. Shading languages were subsequently extended to run in real-time on graphics processing units (GPUs), and now shading languages are widely used in game engines. The final step was the realization that the GPU is a data-parallel computer, and the the shading language could be extended into a general-purpose data-parallel programming language. This enabled a wide variety of applications in high performance computing, such as physical simulation and machine learning, to be run on GPUs.Nowadays, GPUs are the fastest computers in the world.This talk will review the history of shading languages and GPUs, and discuss the broader implications for computing.Bio:Pat Hanrahan is the Canon Professor of Computer Science and Electrical Engineering in the Computer Graphics Laboratory at Stanford University. His research focuses on rendering algorithms, graphics systems, andvisualization.Hanrahan received a Ph.D. in biophysics from the University of Wisconsin-Madison in 1985. As a founding employee at Pixar Animation Studios in the 1980s, Hanrahan led the design of the RenderMan Interface Specification and the RenderMan Shading Language. In1989, he joined the faculty of Princeton University. In 1995, he moved to Stanford University. More recently, Hanrahan served as a co-founder and CTO of Tableau Software. He has received three Academy Awards for Science and Technology, the SIGGRAPH ComputerGraphics Achievement Award, the SIGGRAPH Stephen A. Coons Award, and the IEEE Visualization Career Award. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. In 2019, he received the ACM A. M. Turing Award. 32-123

December 09, 2022

MacORAMa: Optimal Oblivious RAM with Integrity

Neekon Vafa
10:30A
- 12:00P

Location

G-882, Hewlett Room
Add to Calendar 2022-12-09 10:30:00 2022-12-09 12:00:00 America/New_York MacORAMa: Optimal Oblivious RAM with Integrity Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (J. ACM '96), is a primitive that allows a client to perform RAM computations on an external database without revealing any information about the underlying data, even via the access pattern. For a database of size $N$, well-known lower bounds show that a multiplicative overhead of $\Omega(\log N)$ in the number of RAM operations is necessary assuming $O(1)$ client storage. A long sequence of works culminated in the asymptotically optimal construction of Asharov, Komargodski, Lin, and Shi (CRYPTO 2021) with $O(\log N)$ worst-case overhead and $O(1)$ client storage. However, this optimal ORAM construction is only known to be secure in the semi-honest setting, where an adversary is allowed to observe the access patterns but not modify the contents of the database. In the malicious setting, where an adversary is allowed to tamper with the database, this construction and many others in fact become insecure. In this work, we construct an ORAM protocol with worst-case $O(\log N)$ overhead and $O(1)$ client storage that protects against tampering adversaries. By the $\Omega(\log N)$ ORAM lower bound, our construction is asymptotically optimal. We can also interpret our construction as an online memory checker that matches the bandwidth of the best known online memory checkers while additionally hiding the access pattern. To achieve this, we intricately interleave the ORAM construction of Asharov et al. with online and offline memory checking techniques.Joint work with Surya Mathialagan. G-882, Hewlett Room

The Design and Implementation of User-Schedulable Languages

Alex Reinking
EECS Department - UC Berkeley
12:00P
- 1:30P

Location

Seminar Room D463 (Star)
Add to Calendar 2022-12-09 12:00:00 2022-12-09 13:30:00 America/New_York The Design and Implementation of User-Schedulable Languages This thesis details an emerging class of programming language designs, called user-schedulable languages, that provide a safe and productive performance engineering environment for modern, heterogeneous hardware. The defining trait of user-schedulable languages is the division of program specification into two key parts: the algorithm, which defines functionally what is to be computed, and the schedule, which defines how the computation should be carried out. Importantly, algorithms have semantics independent of any schedule, and schedules are semantics-preserving with respect to the algorithm. Thus, programmers are freed from a large class of bugs. Because algorithm languages tend to be functional and domain-specific, the scheduling language can be very expressive. Existing scheduling languages include many program transformations, from accelerator offloading to tricky tiling and interleaving strategies. Multiple schedules can be written for different sets of hardware targets and can be maintained independently from one another.Here, we formally analyze the design of an existing and widely deployed user-schedulable language, Halide, and find and correct several serious bugs and design flaws through this analysis. We also detail both the design and implementation of a new user-schedulable language, Exo, whose design is informed by the lessons learned analyzing Halide. Unlike Halide, which models scheduling as a parameter to a monolithic lowering process, Exo uses a rewrite-based scheduling system. This system doubles as an instruction selection process for custom accelerator hardware; importantly, these instructions can be specified in user programs, rather than inside the compiler itself. We then discuss a novel, high-performance, reference-counting memory management strategy suitable for recursive programs with highly non-local control flow over a (co-)inductive data domain. Finally, practical considerations for language design are discussed; these are lessons learned from maintaining and deploying these systems in practice.Email host for password Seminar Room D463 (Star)

December 12, 2022

December 19, 2022

February 10, 2023

Nearly Optimal Property Preserving Hashing

LaKyah Tyner
Northeastern University
10:30A
- 12:00P

Location

G-882, Hewlett Room
Add to Calendar 2023-02-10 10:30:00 2023-02-10 12:00:00 America/New_York Nearly Optimal Property Preserving Hashing Property-preserving hashing (PPH) consists of a family of compressing hash functions h such that, for any two inputs x, y, we can correctly identify whether some property P(x, y) holds given only the digests h(x), h(y). In a basic PPH, correctness should hold with overwhelming probability over the choice of h when x, y are worst-case values chosen a-priori and independently of h. In an adversarially robust PPH (RPPH), correctness must hold even when x, y are chosen adversarially and adaptively depending on h. Here, we study (R)PPH for the property that the Hamming distance between x and y is at most t.       The notion of (R)PPH was introduced by Boyle, LaVigne and Vaikuntanathan (ITCS ’19), and further studied by Fleischhacker, Simkin (Eurocrypt ’21) and Fleischhacker, Larsen, Simkin (Eurocrypt ’22). In this work, we obtain improved constructions that are conceptually simpler, have nearly optimal parameters, and rely on more general assumptions than prior works. Our results are: We construct information-theoretic non-robust PPH for Hamming distance via syndrome listdecoding of linear error-correcting codes. We provide a lower bound showing that this construction is essentially optimal.We make the above construction robust with little additional overhead, by relying on homomorphic collision-resistant hash functions, which can be constructed from either the discrete-logarithm or the short-integer-solution assumptions. The resulting RPPH achieves improved compression compared to prior constructions, and is nearly optimal.We also show an alternate construction of RPPH for Hamming distance under the minimal assumption that standard collision-resistant hash functions exist. The compression is slightly worse than our optimized construction using homomorphic collision-resistance, but essentially matches the prior state of the art constructions from specific algebraic assumptions.Lastly, we study a new notion of randomized robust PPH (R2P2H) for Hamming distance, which relaxes RPPH by allowing the hashing algorithm itself to be randomized. We give an informationtheoretic construction with optimal parameterSpeaker Bio: LaKyah is a second-year student at Northeastern University working with Daniel Wichs and abhi shelat. She is currently doing work with     property preserving hashing and threshold signatures. G-882, Hewlett Room

February 17, 2023

Functional Commitments for All Functions, under Transparent Setup and Standard Lattice Assumptions

Leo de Castro
10:30A
- 12:00P

Location

RM G-882
Add to Calendar 2023-02-17 10:30:00 2023-02-17 12:00:00 America/New_York Functional Commitments for All Functions, under Transparent Setup and Standard Lattice Assumptions A functional commitment scheme enables a user to concisely commit to a function from a specified family, then later concisely and verifiably reveal values of the function at desired inputs. Useful special cases, which have seen applications across cryptography, include vector commitments and polynomial commitments. To date, functional commitments have been constructed (under falsifiable assumptions) only for functions that are essentially linear.In this talk, we give the first functional commitment scheme for nonlinear functions — indeed, for all functions of any bounded complexity — under a standard setup and a falsifiable assumption. More specifically, the setup is "transparent," requiring only public randomness (and not any trusted entity), and the assumption is the hardness of the standard Short Integer Solution (SIS) lattice problem. Our construction also has other attractive features, including: stateless updates via generic composability; excellent asymptotic efficiency for the verifier, and also for the committer in important special cases like vector and polynomial commitments, thanks to preprocessing (which can even be outsourced to an untrusted party); and post-quantum security, since it is based on SIS. This is joint work with Chris Peikert. RM G-882