Thread by @durumcrustulum

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If the attacker has <= Q guesses, access to the FSB bucket will give no advantage over baseline guessing. Higher Q, greater security loss

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Q: How do you compute password probability?

A: We have a lot of data in the wild now to be able to extract decent probability distributions

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Lunch time!

Next up, "Deep Learning Security Nudges Towards Stronger Cryptography"

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"Stack Overflow considered harmful?" // @FiloSottile

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Whenever users encountered usability issues with crypto libraries, they start googling

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"Never underestimate the power of inertia. That power can be harnessed." HM.

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Nudge theory!

> nudge towards better decisions without restricting options

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Nudge, nudge, do crypto better

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Using machine learning to learn the similar patterns found in crypto posts on Stack Overflow

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Solely the nudges made a difference here, previous experience did not affect the security of solutions 😂

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We believe if SO actually implemented this it would have a significant effect on the security and implementation of software and crypto.

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Example inline nudges on Stack Overflow

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Next up, "CyrpTen: a Machine Learning focused framework for secure computing"

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From Facebook!

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Tensors, but not TensorFlow 😉

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Abstracting crypto-supporting tesnor class types in CrypTen

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https://twitter.com/fischerfel/status/1215719794126868483

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👩‍💻🐍🔥

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Need to improve performance tho

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Say we want to train on a million encrypted images, classify with high accuracy, and complete in a week. (vs minutes unencrypted). If that can be achieved it will be a "step function change" in terms of adopted of encrypted machine learning.

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Next up, "Towards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services Sector" from IBM Research

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~ b r a n d ~

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How can we share data amongst different business areas without breaking some regulation, such as Brazil's GDPR in 2020?

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Requires a multidisciplinary team

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Fully homomorphic encryption allows processing of data without accessing the plaintext, and without sharing. The best known instances use lattices, which lends itself nicely to quantum-resistance.

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Dataset of ~300K entries, real financial transactions over 24 months

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'upsell'

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Everything is done on-prem, no clouds here ☁️

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Some regulations require the data to be _physically_ separated, yet another restriction that makes data sharing hard

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The encrypted data goes to the cloud for processing instead

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github.com/homenc/helib (C++ mostly)

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50x slowdown over unencrypted, which sounds like a lot, but compared to how slow FHE used to be (a few hundred times slower!) this is pretty good

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Short break then we'll be back for the blockchains and distributed ledgers session

Next up, "Challenges and Cryptographic Solutions with Payment-Channel Networks"

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the 🌩️ lightning 🌩️ network!

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Lightning allows us to perform payments offchain ⛓️

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eeep

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On-path adversaries do not learn who pays to whom

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Both issues may be helped if we randomize values at each hop on the path

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Neat! How long are these paths though?

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Lock contracts impl'd from homomorphic one-way functions; Schnorr-based construction

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These security and privacy properties formally proven in the UC framework 👍

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Also useful for cross-currency interoperability

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Goal: constant collateral, where coins are locked only for some delta time, not indefinitely

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https://twitter.com/bascule/status/1215746295928950784

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Still need solutions to address scalable and interoperable routing

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Q: Seems like Alice needs to interact with every entity along the path, does that reveal too much information about them?

A: They get information but not necessarily where it comes from

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Next up, "The Marvellous Universe of Arithmetization-Oriented Primitives"

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Normal hash functions are not efficient inside zkSNARKs. Ideally they would be secure, operates on field elements, and minimizes the number of field mults. AES would be close but it does not minimize mults.

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All the mults are limited to the AES sbox

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In zkSNARKs we are _verifying_ a computation, vs _doing_ a computation

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Non-determinism, and cost of mult is independent of the field size

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Got it down to 2% of regular AES!

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Welp, someone found an attack tho

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The result is called 'Vision':

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And then 'Rescue', over prime fields

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'Rescue'

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Considered very fast and secure in the specific use case as a hash inside SNARKs over 128bit+ fields

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Paper: eprint.iacr.org/2019/426.pdf

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Very last talk, "Detecting Money Laundering Activities via Secure Multi-PartyComputation for Structural Similarities in Flow Networks"

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How can these global-scale money laundering schemes be detected and prevented?

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Currently transaction monitoring is a highly manual process, collaboration across banks is hard because transaction data is private (also regulations).

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Trying a graph-based approach

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#ttants:

ML: money laundering
ML: machine learning

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Represent as adjacency matrix

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"XOR secret computing engine" 👀

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Looks like someone needs a big data pipeline…

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Bloom filters again!

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A whole combination of techniques to tackle this problem!

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Q: Are your techniques resilient to the banks _themselves_ doing money laundering?

A: In general if the bank is malicious and detected, they get fined

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And that's it!

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Real World Crypto 2021 will be in Amsterdam, 2022 will be in Tokyo!

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See you next year! 👋

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