EOEran Omri
Eran Omri
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Eran Omri has a total of 61 co-authors. Here are the top co-authors by number of shared publications:
- Bar Alon
Tel-Aviv University
11 shared publications - Amos Beimel
Ben-Gurion University of the Negev
10 shared publications Iftach Haitner
6 shared publications- Iftach Haitner
The Blavatnik School of Computer Science, Tel Aviv University
6 shared publications - Yehuda Lindell
Coinbase
5 shared publications Ilan Orlov
Department of Computer Science, Ben Gurion University, Be’er Sheva, Israel
4 shared publications
Latest Publications
- 6 November 2026
- Published by Springer Nature
- p. 60-83
Deniable Secret Sharing: Towards Practical Faking
- 6 November 2026
- Published by Springer Nature
- p. 60-83
Deniable Secret Sharing: Towards Practical Faking
- Open Access
- 4 June 2026
- Published by Association for Computing Machinery (ACM)
- p. 1-17
MYao: Efficient Multiparty “Yao” Garbled Circuits with Row Reduction and Half Gates
- 4 June 2026
- Published by Association for Computing Machinery (ACM)
- p. 1-17
Open AccessMYao: Efficient Multiparty “Yao” Garbled Circuits with Row Reduction and Half Gates
- 3 December 2024
- Published by Springer Nature
- p. 548-580
New Upper Bounds for Evolving Secret Sharing via Infinite Branching Programs
- 3 December 2024
- Published by Springer Nature
- p. 548-580
New Upper Bounds for Evolving Secret Sharing via Infinite Branching Programs
- 16 August 2024
- Published by Springer Nature
- p. 74-108
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
- 16 August 2024
- Published by Springer Nature
- p. 74-108
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
- 29 April 2024
- Published by Springer Nature
- p. 32-61
Can Alice and Bob Guarantee Output to Carol?
- 27 November 2023
- Published by Springer Nature
- p. 156-185
Three Party Secure Computation with Friends and Foes
- 27 November 2023
- Published by Springer Nature
- p. 156-185
Three Party Secure Computation with Friends and Foes
- 27 November 2023
- Published by Springer Nature
- p. 94-123
On Secure Computation of Solitary Output Functionalities with and Without Broadcast
- 27 November 2023
- Published by Springer Nature
- p. 94-123
On Secure Computation of Solitary Output Functionalities with and Without Broadcast
- 7 June 2023
- Published by Springer Nature in Journal of Cryptology
- Vol. 36 (3) , 1-41
On the Power of an Honest Majority in Three-Party Computation Without Broadcast
- 7 June 2023
- Published by Springer Nature in Journal of Cryptology
- Vol. 36 (3) , 1-41
On the Power of an Honest Majority in Three-Party Computation Without Broadcast
- 7 June 2023
- Published by Springer Nature in Journal of Cryptology
- Vol. 36 (3) , 1-39
Almost-Optimally Fair Multiparty Coin-Tossing with Nearly Three-Quarters Malicious
- 7 June 2023
- Published by Springer Nature in Journal of Cryptology
- Vol. 36 (3) , 1-39
Almost-Optimally Fair Multiparty Coin-Tossing with Nearly Three-Quarters Malicious
- Open Access
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
In this work, we introduce the Gulliver multi-party computation model (GMPC). The GMPC model considers a single highly powerful party, called the server or Gulliver, that is connected to $n$ users over a star topology network (alternatively formulated as a full network, where the server can block any message). The users are significantly less powerful than the server, and, in particular, should have both computation and communication complexities that are polylogarithmic in $n$. Protocols in the GMPC model should be secure against malicious adversaries that may corrupt a subset of the users and/or the server. Designing protocols in the GMPC model is a delicate task, since users can only hold information about polylog(n) other users (and, in particular, can only communicate with polylog(n) other users). In addition, the server can block any message between any pair of honest parties. Thus, reaching an agreement becomes a challenging task. Nevertheless, we design generic protocols in the GMPC model, assuming that at most $\alpha<1/6$ fraction of the users may be corrupted (in addition to the server). Our main contribution is a variant of Feige's committee election protocol [FOCS 1999] that is secure in the GMPC model. Given this tool we show: 1. Assuming fully homomorphic encryption (FHE), any computationally efficient function with $O\left(n\cdot polylog(n)\right)$-size output can be securely computed in the GMPC model. 2. Any function that can be computed by a circuit of $O(polylog(n))$ depth, $O\left(n\cdot polylog(n)\right)$ size, and bounded fan-in and fan-out can be securely computed in the GMPC model without assuming FHE. 3. In particular, sorting can be securely computed in the GMPC model without assuming FHE. This has important applications for the shuffle model of differential privacy, and resolves an open question of Bell et al. [CCS 2020].Open AccessMPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably
In this work, we introduce the Gulliver multi-party computation model (GMPC). The GMPC model considers a single highly powerful party, called the server or Gulliver, that is connected to $n$ users over a star topology network (alternatively formulated as a full network, where the server can block any message). The users are significantly less powerful than the server, and, in particular, should have both computation and communication complexities that are polylogarithmic in $n$. Protocols in the GMPC model should be secure against malicious adversaries that may corrupt a subset of the users and/or the server. Designing protocols in the GMPC model is a delicate task, since users can only hold information about polylog(n) other users (and, in particular, can only communicate with polylog(n) other users). In addition, the server can block any message between any pair of honest parties. Thus, reaching an agreement becomes a challenging task. Nevertheless, we design generic protocols in the GMPC model, assuming that at most $\alpha<1/6$ fraction of the users may be corrupted (in addition to the server). Our main contribution is a variant of Feige's committee election protocol [FOCS 1999] that is secure in the GMPC model. Given this tool we show: 1. Assuming fully homomorphic encryption (FHE), any computationally efficient function with $O\left(n\cdot polylog(n)\right)$-size output can be securely computed in the GMPC model. 2. Any function that can be computed by a circuit of $O(polylog(n))$ depth, $O\left(n\cdot polylog(n)\right)$ size, and bounded fan-in and fan-out can be securely computed in the GMPC model without assuming FHE. 3. In particular, sorting can be securely computed in the GMPC model without assuming FHE. This has important applications for the shuffle model of differential privacy, and resolves an open question of Bell et al. [CCS 2020].