We propose a new notion of secure multiparty computation aided by a computationally-powerful but untrusted "cloud" server. In this notion that we call on-the-fly multiparty computation (MPC), the cloud can non-interactively perform arbitrary, dynamically chosen computations on data belonging to arbitrary sets of users chosen on-the-fly. All user's input data and intermediate results are protected from snooping by the cloud as well as other users. This extends the standard notion of fully homomorphic encryption (FHE), where users can only enlist the cloud's help in evaluating functions on their own encrypted data.
We propose a new notion of secure multiparty computation aided by a computationally-powerful but untrusted "cloud" server. In this notion that we call on-the-fly multiparty computation (MPC), the cloud can non-interactively perform arbitrary, dynamically chosen computations on data belonging to arbitrary sets of users chosen on-the-fly. All user's input data and intermediate results are protected from snooping by the cloud as well as other users. This extends the standard notion of fully homomorphic encryption (FHE), where users can only enlist the cloud's help in evaluating functions on their own encrypted data.
Secure multi-party computation (MPC) is one of the most important primitives in cryptography. Several directions to construct and improve MPC protocols have been studied over the past decades. The majority of existing MPC protocols rely on assumptions such as every party communicates with every other party or every party needs to know every other party and the computation algorithm well in advance. Such assumptions can be difficult to achieve in modern large scale networks. This thesis explores two distinct lines of work leading to new practical models in response to these scenarios.First, we examine communication locality, the total number of other parties that each party communicates with in the protocol, as a new measure for an MPC protocol. Although progress has been made, the question of achieving low communication locality and round complexity at the same time for adaptive adversaries corrupting t < n/2 parties remains open. We show that by assuming a public-key infrastructure (PKI) and a symmetric-key infrastructure (SKI) the above question can be answered affirmatively, under standard assumptions. In particular, we describe a protocol with polylogarithmic communication locality and round complexity which tolerates up to t < n/2 adaptive corruptions. Our results are based on a new model of hidden random graphs obtained from the SKI, and using them to emulate a complete network in polylogarithmic rounds and polylogarithmic locality.We then examine multi-key fully homomorphic encryption (MFHE) schemes, which allow computation on data encrypted under different public keys chosen independently of each other. One of the main problems left in MFHE has been on how to deal with malicious users without trusted setup assumptions. We show how this can be done through circuit privacy, which guarantees that even if both ciphertexts and public keys are not well-formed, no information is revealed regarding the computation, other than what follows from the output on some well-formed inputs. Generalizing the result for circuit-private FHE, we provide a framework for adding circuit privacy to existing MFHE schemes.Finally, we incorporate the circuit privacy in a variant of server-assisted MPC, called on-the-fly MPC, where a server or a “cloud” performs an arbitrary dynamically chosen computation on a subset of data uploaded by multiple clients and encrypted under different keys on-the-fly. Circuit privacy allows the server to keep the algorithm used in the computation private even from a malicious adversary corrupting any number of clients. In particular, we construct a three-round on-the-fly MPC protocol with circuit privacy against malicious clients without the trusted setup.
We construct protocols for secure multiparty computation with the help of a computationally powerful party, namely the “cloud”. Our protocols are simultaneously ecient in a number of metrics: • Rounds: our protocols run in 4 rounds in the semi-honest setting, and 5 rounds in the malicious setting. • Communication: the number of bits exchanged in an execution of the protocol is independent of the complexity of function f being computed, and depends only on the length of the inputs and outputs. • Computation: the computational complexity of all parties is independent of the complexity of the function f, whereas that of the cloud is linear in the size of the circuit computing f. In the semi-honest case, our protocol relies on the “ring learning with errors” (RLWE) assumption, whereas in the malicious case, security is shown under the Ring LWE assumption as well as the existence of simulation-extractable NIZK proof systems and succinct non-interactive arguments. In the malicious setting, we also relax the communication and computation requirements above, and only require that they be “small” ‐ polylogarithmic in the computation size and linear in the size of the joint size of the inputs. Our constructions leverage the key homomorphic property of the recent fully homomorphic encryption scheme of Brakerski and Vaikuntanathan (CRYPTO 2011, FOCS 2011). Namely, these schemes allow combining encryptions of messages under dierent keys to produce an encryption (of the sum of the messages) under the sum of the keys. We also design an ecient, non-interactive threshold decryption protocol for these fully homomorphic encryption schemes.
Secure multi-party computation (MPC) is one of the most important primitives in cryptography. Several directions to construct and improve MPC protocols have been studied over the past decades. The majority of existing MPC protocols rely on assumptions such as every party communicates with every other party or every party needs to know every other party and the computation algorithm well in advance. Such assumptions can be difficult to achieve in modern large scale networks. This thesis explores two distinct lines of work leading to new practical models in response to these scenarios.First, we examine communication locality, the total number of other parties that each party communicates with in the protocol, as a new measure for an MPC protocol. Although progress has been made, the question of achieving low communication locality and round complexity at the same time for adaptive adversaries corrupting t l n/2 parties remains open. We show that by assuming a public-key infrastructure (PKI) and a symmetric-key infrastructure (SKI) the above question can be answered affirmatively, under standard assumptions. In particular, we describe a protocol with polylogarithmic communication locality and round complexity which tolerates up to t l n/2 adaptive corruptions. Our results are based on a new model of hidden random graphs obtained from the SKI, and using them to emulate a complete network in polylogarithmic rounds and polylogarithmic locality.We then examine multi-key fully homomorphic encryption (MFHE) schemes, which allow computation on data encrypted under different public keys chosen independently of each other. One of the main problems left in MFHE has been on how to deal with malicious users without trusted setup assumptions. We show how this can be done through circuit privacy, which guarantees that even if both ciphertexts and public keys are not well-formed, no information is revealed regarding the computation, other than what follows from the output on some well-formed inputs. Generalizing the result for circuit-private FHE, we provide a framework for adding circuit privacy to existing MFHE schemes.Finally, we incorporate the circuit privacy in a variant of server-assisted MPC, called on-the-fly MPC, where a server or a “cloud” performs an arbitrary dynamically chosen computation on a subset of data uploaded by multiple clients and encrypted under different keys on-the-fly. Circuit privacy allows the server to keep the algorithm used in the computation private even from a malicious adversary corrupting any number of clients. In particular, we construct a three-round on-the-fly MPC protocol with circuit privacy against malicious clients without the trusted setup.
Abstract. Fully homomorphic encryption (FHE) enables secure computation over the encrypted data of a single party. We explore how to extend this to multiple parties, using threshold fully homomorphic encryption (TFHE). In such scheme, the parties jointly generate a common FHE public key along with a secret key that is shared among them; they can later cooperatively decrypt ciphertexts without learning anything but the plaintext. We show how to instantiate this approach efficiently, by extending the recent FHE schemes of Brakerski, Gentry and Vaikuntanathan (CRYPTO ’11, FOCS ’11, ITCS ’12) based on the (ring) learning with errors assumption. Our main tool is to exploit the property that such schemes are additively homomorphic over their keys. Using TFHE, we construct simple multiparty computation protocols secure against fully malicious attackers, tolerating any number of corruptions, and providing security in the universal composability framework. Our protocols have the following properties: Low interaction: 3 rounds of interaction given a common random string, or 2 rounds with a public-key infrastructure. Low communication: independent of the function being computed (proportional to just input and output sizes). Cloud-assisted computation: the bulk of the computation can be efficiently outsourced to an external entity (e.g. a cloud service) so that the computation of all other parties is independent of the complexity of the evaluated function. 1
The emergence of cloud computing provides a new computing paradigm for users -- massive and complex computing tasks can be outsourced to cloud servers. However, the privacy issues also follow. Fully homomorphic encryption shows great potential in privacy-preserving computation, yet it is not ready for practice. At present, secure multiparty computation (MPC) remains mainly approach to deal with sensitive data. In this paper, following the secret sharing based MPC paradigm, we propose a secure 2-party computation scheme, in which cloud servers can securely evaluate functions with high efficiency. We first propose the multiplicative secret sharing (MSS) based on typical additive secret sharing (ASS). Then, we design protocols to switch shared secret between MSS and ASS, based on which a series of protocols for comparison and nearly all of the elementary functions are proposed. We prove that all the proposed protocols are Universally Composable secure in the honest-but-curious model. Finally, we will show the remarkable progress of our protocols on both communication efficiency and functionality completeness.
Multiparty fully homomorphic encryption (MPFHE) is a generalization of (multi-key) fully homomorphic encryption ((MK)FHE) that lives on the cusp between multiparty computation (MPC) and FHE, enabling a computation over encrypted data using multiple keys. However, contrary to MKFHE, it seeks to reduce the noise inflation based on the number of parties by allowing the parties to first compute shared data in MPC before executing the computation in FHE. However, many works use specific constructions, e.g. Chang-Li, Koirala et. al., and Ma et. al., reliant on the behaviour of a particular scheme and do not consider security against colluding servers and parties, e.g. Passelègue-Stehlé, Okada-Takagi, and Mouchet et. al. In this work we present a new framework that is secure against collusion between servers and parties and allows the use of a broad spectrum of MPC and FHE protocols, while eliminating the noise inflation based on the participating number of parties. We then show a case study of this using the FINAL scheme and show that we reduce the required key material by 60–99.9% compared to the MKFHE FINAL scheme, FINALLY, and 65–70% compared to the Park-Rovira scheme in all cases, and by up to 71% compared to the AKÖ scheme when there are 4 or more parties. Moreover, we reduce the bootstrapping time for the AKÖ, Park-Rovira, and KMS schemes by 75–99.7%.