IACR News
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31 January 2023
Meta, Menlo Park, CA, USA
Challenges and intern projects include incorporating approaches such as multi-party computation, homomorphic encryption, trusted execution environments, differential privacy, and federated learning to develop privacy-focused solutions while maintaining performance at massive scale, including cryptographic protocols, algorithms & tooling for machine learning or analytics. Research projects may include developing new or improving existing privacy-preserving solutions for areas such as: private record linkage, privacy-preserving ML and analytics.
For more details and to apply: https://www.metacareers.com/jobs/881989909611952/
Closing date for applications:
Contact: Gaven Watson
More information: https://www.metacareers.com/jobs/881989909611952/
30 January 2023
Tarun Chitra, Matheus V. X. Ferreira, Kshitij Kulkarni
Luciano Freitas, Andrei Tonkikh, Adda-Akram Bendoukha, Sara Tucci-Piergiovanni, Renaud Sirdey, Oana Stan, Petr Kuznetsov
Homomorphic Sortition relies on Threshold Fully Homomorphic Encryption (ThFHE) and is tailored to proof-of-stake (PoS) blockchains, with several important optimizations with respect to prior proposals. In particular, unlike most existing SSLE protocols, it works with arbitrary stake distributions and does not require a user with multiple coins to be registered multiple times. Our protocol is highly parallelizable and can be run completely off-chain after setup.
Some blockchains require a sequence of rounds to have non-repeating leaders. We define a generalization of SSLE, called Secret Leader Permutation (SLP) in which the application can choose how many non-repeating leaders should be output in a sequence of rounds and we show how Homomorphic Sortition also solves this problem.
Gabrielle De Micheli, Duhyeong Kim, Daniele Micciancio, Adam Suhl
Vahid Amin-Ghafari, Mohammad Ali Orumiehchiha, Saeed Rostami
Ripon Patgiri, Laiphrakpam Dolendro Singh
Bologna, Italia, 25 May - 26 May 2023
Submission deadline: 24 February 2023
Notification: 7 April 2023
Neuchâtel, Switzerland, 27 June - 30 June 2023
Submission deadline: 17 January 2023
Notification: 27 April 2023
Chicago, USA, 2 July - 8 July 2023
Submission deadline: 5 March 2023
Guangzhou, China, 4 December - 8 December 2023
29 January 2023
Lyon, France, 23 April 2023
Submission deadline: 7 March 2023
Canterbury, United Kingdom, 14 August - 16 August 2023
Submission deadline: 3 March 2023
Yokohama, Japan, 29 August - 31 August 2023
Submission deadline: 26 March 2023
Notification: 30 May 2023
28 January 2023
Ling Sun, Meiqin Wang
Kyle Storrier, Adithya Vadapalli, Allan Lyons, Ryan Henry
Alan Szepieniec, Alexander Lemmens, Jan Ferdinand Sauer, Bobbin Threadbare
The context motivating this design is the recursive verification of STARKs. This context imposes particular design constraints, and therefore the hash function's arithmetization is discussed at length.
Jonathan Komada Eriksen, Lorenz Panny, Jana Sotáková, Mattia Veroni
Georg Land, Adrian Marotzke, Jan Richter-Brockmann, Tim Güneysu
27 January 2023
Anamaria Costache, Lea Nürnberger, Rachel Player
Runchao Han, Jiangshan Yu
In this paper, we formalise a new property, delivery-fairness, to quantify the advantage. In particular, we distinguish two aspects of delivery-fairness, namely length-advantage, i.e., how many random outputs an adversary can learn earlier than correct participants, and time-advantage, i.e., how much time an adversary can learn a given random output earlier than correct participants. In addition, we prove the lower bound of delivery-fairness showing optimal guarantee. We further analyse the delivery-fairness guarantee of state-of-the-art DRBs and discuss insights, which, we show through case studies, could help improve delivery-fairness of existing systems to its optimal.