Sharemind: A framework for fast privacy-preserving computations. Gathering and processing sensitive data is a difficult task. In fact, there is no common recipe for building the necessary information systems. In this paper, we present a provably secure and efficient general-purpose computation system to address this problem. Our solution—Sharemind—is a virtual machine for privacy-preserving data processing that relies on share computing techniques. This is a standard way for securely evaluating functions in a multi-party computation environment. The novelty of our solution is in the choice of the secret sharing scheme and the design of the protocol suite. We have made many practical decisions to make large-scale share computing feasible in practice. The protocols of Sharemind are information-theoretically secure in the honest-but-curious model with three computing participants. Although the honest-but-curious model does not tolerate malicious participants, it still provides significantly increased privacy preservation when compared to standard centralised databases.

References in zbMATH (referenced in 16 articles )

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  1. Bacelar Almeida, José Carlos; Barbosa, Manuel; Barthe, Gilles; Pacheco, Hugo; Pereira, Vitor; Portela, Bernardo: A formal treatment of the role of verified compilers in secure computation (2022)
  2. Abspoel, Mark; Dalskov, Anders; Escudero, Daniel; Nof, Ariel: An efficient passive-to-active compiler for honest-majority MPC over rings (2021)
  3. Patra, Arpita; Ravi, Divya: On the exact round complexity of secure three-party computation (2021)
  4. Ersoy, Oğuzhan; Pedersen, Thomas Brochmann; Anarim, Emin: Homomorphic extensions of CRT-based secret sharing (2020)
  5. Li, Ruinian; Xiao, Yinhao; Zhang, Cheng; Song, Tianyi; Hu, Chunqiang: Cryptographic algorithms for privacy-preserving online applications (2018)
  6. Büscher, Niklas; Franz, Martin; Holzer, Andreas; Veith, Helmut; Katzenbeisser, Stefan: On compiling Boolean circuits optimized for secure multi-party computation (2017)
  7. Butler, David; Aspinall, David; Gascón, Adrià: How to simulate it in Isabelle: towards formal proof for secure multi-party computation (2017)
  8. Dagdelen, Özgür; Mohassel, Payman; Venturi, Daniele: Rate-limited secure function evaluation (2016)
  9. Hemenway, Brett; Lu, Steve; Ostrovsky, Rafail; Welser, William IV: High-precision secure computation of satellite collision probabilities (2016)
  10. Kumaresan, Ranjit; Raghuraman, Srinivasan; Sealfon, Adam: Network oblivious transfer (2016)
  11. Kamara, Seny; Mohassel, Payman; Raykova, Mariana; Sadeghian, Saeed: Scaling private set intersection to billion-element sets (2014) ioport
  12. Choi, Seung Geol; Hwang, Kyung-Wook; Katz, Jonathan; Malkin, Tal; Rubenstein, Dan: Secure multi-party computation of Boolean circuits with applications to privacy in on-line marketplaces (2012)
  13. Bain, Alex; Mitchell, John; Sharma, Rahul; Stefan, Deian; Zimmerman, Joe: A domain-specific language for computing on encrypted data. (Invited talk) (2011)
  14. Loftus, Jake; Smart, Nigel P.: Secure outsourced computation (2011)
  15. Zhang, Bingsheng: Generic constant-round oblivious sorting algorithm for MPC (2011)
  16. Bogdanov, Dan; Laur, Sven; Willemson, Jan: Sharemind: A framework for fast privacy-preserving computations (2008) ioport