Editing the Immutable Blockchain
Presenter Name:Giuseppe Ateniese
A new disruptive approach will be introduced that makes the Blockchain viable for enterprise use
by addressing privacy and scalability issues. The new Blockchain remains decentralized and immutable but
a "plan b" is supported if things go wrong. This technology has been co-developed with Accenture and its
announcement made international news and appeared in several news outlets (NYT, FT, Forbes, Reuters,
Fortune, MIT Tech Review, TechRepublic, etc.).
This talk is about FinTech, Security, and Privacy.
Giuseppe Ateniese is the David and GG Farber Endowed Chair in Computer Science and department chair at Stevens Institute of Technology. He was with Sapienza-University of Rome (Italy) and Assistant/Associate Professor at Johns Hopkins University (USA), and one of the founders of the JHU Information Security Institute. He was a researcher at IBM Zurich Research lab (Switzerland) and scientist at the Information Sciences Institute of the University of Southern California (USA). He also briefly worked as visiting professor at Microsoft in Redmond (USA). He received the NSF CAREER Award for his research in privacy and security, and the Google Faculty Research Award and the IBM Faculty Award for his research on cloud security.
Computations on Encrypted Data and Privacy
Presenter Name:David Pointcheval
Functional encryption is a nice tool that bridges the gap between usability
and privacy when providing access to huge databases: while being encrypted,
aggregated information is available with a fine-tuned control by the owner
of the database who can specify the functions he allows users to compute on
Unfortunately, giving access to several functions might leak too much
information on the database, since once the decryption capability is given
for a specific function, this is for an unlimited number of ciphertexts. In
addition, this is difficult to control the functional decryption keys that
are distributed, and collusions should be considered.
In the particular case of the inner-product, if too many functional keys
are known by the adversary, he can completely learn the plaintexts.
On the other hand, the major applications that make use of inner-products,
such as machine-learning, need to compute many of them.
We will thus discuss on functional encryption and some approaches to
David Pointcheval obtained his PhD in Computer Science from the University of Caen in 1996.
Since 1998, he has been a CNRS researcher, in the Computer Science Department at Ecole normale sup茅rieure, Paris, France, in the Cryptography Team, that he has been leading since 2005. This team has also been associated to Inria since 2008.
Since 2017, he has been the chair of the Computer Science Department at ENS.His research focuses on provable security of cryptographic primitives and protocols.He is an author of more than 130 international conference and journal papers, and an inventor of a dozen of patents.He has been one of the nine elected directors in the board of IACR, for 9 years.He has been program chair for several international conferences in cryptography, including PKC 2010 and Eurocrypt 2012.He has recently been awarded an ERC Advanced Grant, from the European Commission, on the Privacy for the Cloud.