[Busec] Tomorrow, Friday, April 17: Charles River Crypto Day @ Northeastern
goldbe at cs.bu.edu
Thu Apr 16 12:19:06 EDT 2015
---------- Forwarded message ----------
From: Nir Bitansky <nbitansky at gmail.com>
Date: Thu, Apr 16, 2015 at 11:08 AM
Please join us for the next installment of the Charles River Crypto Day
Friday, April 17 at Northeastern University. See below for location info,
schedule and abstracts.
-- Daniel, Yael, Vinod, Nir
103 Churchill Hall
Northeastern University Boston, MA 02115
Program:9:30 – 10:00.Introduction/Coffee10:00 – 11:00.
Leo Reyzin, BU
*Wyner’s Wire-Tap Channel, Forty Years Later*
11:30 – 12:30.Christopher Fletcher, MIT
*Onion ORAM: A Constant Bandwidth ORAM using Additively Homomorphic
Encryption*12:30 – 2:00.Lunch (provided)2:00 – 3:00.Daniele Micciancio, UCSD
*FHEW: Bootstrapping Homomorphic Encryption in less than a second*3:30 –
4:30.Kobbi Nissim, Ben-Gurion University and CRCS at Harvard
*Learning under Differential Privacy*Abstracts:
*Speaker: Leo Reyzin*
*Wyner’s Wire-Tap Channel, Forty Years Later*
Wyner’s information theory paper “The Wire-Tap Channel” turns forty this
year. Its importance is underappreciated in cryptography, where its
intellectual progeny includes pseudorandom generators, privacy
amplification, information reconciliation, and many flavors of randomness
extractors (including plain, strong, fuzzy, robust, nonmalleable,
source-private, local, and reusable). Focusing mostly on privacy
amplification and fuzzy extractors, I will demonstrate the connection from
Wyner’s paper to today’s research, including work on program obfuscation. I
will conclude with some recent results on the feasibility of fuzzy
extractors, based on joint work with Benjamin Fuller and Adam Smith.
*Speaker: Christopher Fletcher*
*Title: Onion ORAM: A Constant Bandwidth ORAM using Additively Homomorphic
Oblivious RAM (ORAM) is a cryptographic primitive that obfuscates a
client’s access pattern (address, data, read/write) to an untrusted memory
source. In addition to its traditional application to outsourced storage,
ORAM has proven to be an important component in the Cryptographic “swiss
army knife” — finding uses in Garbled RAM, Secure Computation, Proofs of
Retrievability, and more.
In this talk I will discuss Onion ORAM, a constant bandwidth ORAM that uses
poly-logarithmic server computation to circumvent the well-known
logarithmic lower bound in ORAM bandwidth. In addition to being constant
bandwidth, Onion ORAM achieves constant client and server storage blowups —
asymptotically optimal for each category — and does so without relying on
Fully Homomorphic Encryption. In particular, we only require an Additively
Homomorphic Encryption scheme with constant ciphertext blowup such as the
Damgard-Jurik cryptosystem. We will extend the scheme to be secure against
a malicious server using standard assumptions. To the best of our
knowledge, Onion ORAM is the first concrete instantiation of a
constant-bandwidth ORAM with poly-logarithmic computation (even for the
Joint work with Ling Ren, Srini Devadas, Marten van Dijk, Elaine Shi and
*Speaker: Daniele Micciancio*
*Title: FHEW: Bootstrapping Homomorphic Encryption in less than a second*
The main bottleneck affecting the efficiency of all known fully homomorphic
encryption (FHE) schemes is Gentry’s bootstrapping procedure, which is
required to refresh noisy ciphertexts and keep computing on encrypted data.
We present a new method to homomorphically compute simple bit operations,
and refresh (bootstrap) the resulting output, which runs on a personal
computer in just about half a second.
Join work with Leo Ducas, to appear in Eurocrypt 2015
*Speaker: Kobbi Nissim*
*Title: Learning under Differential Privacy*
Learning is a task that abstracts many of the computations performed over
large collections of sensitive individual information, hence natural to
examine in conjunction with differential privacy. Predating differential
privacy, in 2005 Blum, Dwork, McSherry and Nissim showed that any concept
class that is learnable in Kearns’ model of statistical queries is also
learnable with privacy. The concept of Private Learning formalized by
Kasiviswanathan et al. in 2008 as the conjunction of PAC learning and
differential privacy. They showed that any concept class |C| can be learned
privately with O(log|C|) samples, via a construction that is somewhat
analogous to the Occam Razor (non-private) learner.
Investigating the gap between the sample complexity and computational
complexity of private and non-private learners resulted in a rich picture
that we will highlight in the talk. In particular, we will examine some of
the lower bound and upper bound techniques used in this context, and
explore some of the ways to mitigate the costs of private learners. Time
permitting, we will see relationships between private learning and other
tasks, such as median computation and data sanitization.
Based on joint works with: Amos Beimel, Avrim Blum, Hai Brenner, Mark Bun,
Cynthia Dwork, Shiva Kasiviswanathan, Homin Lee, Frank McSherry, Sofya
Raskhodnikova, Adam Smith, Uri Stemmer, and Salil Vadhan.
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Computer Science, Boston University
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