International audienceWe consider Initial Public Offering (IPO) on blockchains while preserving privacy using Secure Multiparty Computation (MPC), which allows participants to perform a computation on secret data. We provide "MPC as a service", where users requiring a computation distributes shares of their data to MPC workers who run an MPC protocol on the shares and return the result. Previous work by Benhamouda et al. considered IPO over Hyperledger Fabric. We improve by providing a tighter and easier integration of MPC protocol in Fabric using the MPC library SCALE-MAMBA. We explain the obtained security benefits and experimental results are provided
Privacy is a growing concern in the digital world as more information becomes digital every day. Oft...
Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is alm...
In recent years, Multiparty computation as a service (MPCSaaS) is gaining popularity as a...
International audienceWe consider Initial Public Offering (IPO) on blockchains while preserving priv...
Privacy is important both for individuals and corporations. While individuals want to keep their per...
Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC...
Secure multi-party computation (MPC) allows two or more parties to compute an arbitrary function on ...
Permissioned blockchains have resulted in some unlikely collaborations between organizations that wo...
The age of internet of things, where each device and application double up as a source of data has l...
Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to com...
In this work, we introduce FANNG-MPC, a versatile secure multi-party computation framework capable t...
International audienceSecure multiparty computation (MPC) has recently been increasingly adopted to ...
Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a fun...
Protecting users' privacy in digital systems becomes more complex and challenging over time, as the ...
When it comes to trading and auctions, a party would not want to reveal their intention to buy or se...
Privacy is a growing concern in the digital world as more information becomes digital every day. Oft...
Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is alm...
In recent years, Multiparty computation as a service (MPCSaaS) is gaining popularity as a...
International audienceWe consider Initial Public Offering (IPO) on blockchains while preserving priv...
Privacy is important both for individuals and corporations. While individuals want to keep their per...
Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC...
Secure multi-party computation (MPC) allows two or more parties to compute an arbitrary function on ...
Permissioned blockchains have resulted in some unlikely collaborations between organizations that wo...
The age of internet of things, where each device and application double up as a source of data has l...
Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to com...
In this work, we introduce FANNG-MPC, a versatile secure multi-party computation framework capable t...
International audienceSecure multiparty computation (MPC) has recently been increasingly adopted to ...
Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a fun...
Protecting users' privacy in digital systems becomes more complex and challenging over time, as the ...
When it comes to trading and auctions, a party would not want to reveal their intention to buy or se...
Privacy is a growing concern in the digital world as more information becomes digital every day. Oft...
Existing work on privacy-preserving machine learning with Secure Multiparty Computation (MPC) is alm...
In recent years, Multiparty computation as a service (MPCSaaS) is gaining popularity as a...