We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users' public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function.
We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users' public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function.
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.
Abstract. Secure Multiparty Computation (SMC) enables a set of users to evaluate certain functionalities on their respective inputs while keeping these inputs encrypted throughout the computation. Inmanyscenarios, however, outsourcingthesecomputations toanuntrustedserver is desirable, sothattheservercanperform thecomputationonbehalfoftheusers.Unfortunately, existing solutions are either inefficient, rely heavily on user interaction, or require the inputs to be encrypted under the same key—drawbacks making the employment in practice very limited. We propose the first general-purpose construction that avoids all these drawbacks: it is efficient, it requires no user interaction whatsoever (except for data up- and download), and it allows evaluating any dynamically chosen function on inputs encrypted under different independent public keys. Our solution assumes the existence of two non-colluding but untrusted servers that jointly perform the computation by means of a cryptographic protocol. This protocol is provably secure in the semi-honest model. We demonstrate the applicability of our result in two real-world scenarios from different domains: Privacy-Preserving Face Recognition and Private Smart Metering. Finally, we give a performance analysis of our general-purpose construction to highlight its practicability.
The rapid development of cloud computing has probably benefited each of us. However, the privacy risks brought by untrusty cloud servers arise the attention of more and more people and legislatures. In the last two decades, plenty of works seek the way of outsourcing various specific tasks while ensuring the security of private data. Although the addition and multiplication are enough for implementing any functions, the direct utilization of existing schemes like homomorphic encryption will lead to significant efficiency and accuracy loss, which is not suitable for outsourcing computation tasks. The tasks to be outsourced are endless, however, the involved calculations are similar. In this paper, inspired by additive secret sharing and multiplicative secret sharing technologies, we construct a series of novel protocols which support the common secure calculations on numbers (e.g., basic elementary functions) or matrices (e.g., solve eigenvectors) in arbitrary n number of servers (n≥2), and the n-party protocols ensure the security of the original data even if n−1 servers collude. All protocols we designed only need constant interaction rounds, and we demonstrate them under universally composability security. We believe that these protocols can provide a new basic tool for actual outsourced tasks.
Outsourcing computation to cloud server has recently become popular in cloud computing. Cloud computing technologies enable clients with limited computational resources to outsource their massive computations to powerful cloud servers. Outsourcing computation has some new concerns, such as outsourced data and result privacy, verifiability and efficiency. Matrix multiplication is one of the most basic computational problems. In this paper, we are motivated to design a secure and efficient protocol for outsourcing the massive matrix multiplication computations to the cloud server. The existing works are mostly under amortized model that have an expensive phase as offline mode. Our proposed scheme does not have any expensive phase. So the client can outsource its matrices in online mode in which the efficiency of outsourcing will be increased. In our scheme, the client encrypts two matrices and sends them to a semi-honest cloud server. The cloud server computes matrix multiplication and a proof. After that, it sends the encrypted result to the client and finally the client checks the validity of the computation and decrypts the result. Our proposed scheme achieves privacy protection of outsourced data and multiplication result, unforgeability of proof, verification and high efficiency.
Web-servers are a dominant medium of modern communication and thus process vast amounts of highly sensitive, private data. Social networks, online auctions, and all kinds of cloud services constitute popular examples of online services complying with this paradigm. Due to many alarming scandals regarding the misuse of private data, IT security solutions protecting this sensitive data (for example by hiding it through encryption) are indispensable. Ideally, web-servers should be able to compute on private data without seeing it - we refer to this as the "Secure Outsourcing of Computation". Most notably, the cryptographic primitive of Homomorphic Encryption (HE) enables the web-servers to process data in the encrypted domain, which thereby hides/protects all private data as it is never available in the clear. More precisely, HE is a type of encryption that allows for the computation of certain functionalities on encrypted data without knowledge of the decryption key. Depending on the HE scheme, various functionalities can be evaluated in the encrypted domain, ranging from additions or multiplications only (Group Homomorphic Encryption, GHE), to virtually any computable functionality (Fully Homomorphic Encryption, FHE). We provide a clean foundational framework for HE that permits security characterizations for a large class of HE schemes and shows strong similarities between GHE and FHE. We study these characterizations in order to assess the limits of HE, and show that GHE is obsolete in the quantum world, meaning that any GHE scheme can be broken by a quantum computer. Together with our newly constructed universal blueprint that encompasses virtually any GHE scheme, we know exactly how these schemes must be designed and what the underlying hardness assumptions must look like. To some extent, this rounds off the topic of GHE both in the standard and quantum model of computation. In the standard model (which is presently assumed to be the most realistic model of computation), GHE is still highly attractive as it provides very efficient solutions to a variety of practical problems. On the basis of our foundational framework, we construct new GHE schemes with unique features and show their employability in practical applications. Most importantly, we use the special features of one of these schemes and develop a novel technique to achieve a practically efficient solution to the problem of securely outsourcing computations. Our construction allows multiple users to send encryptions of their private data under their own keys to a distrusted web-server that can then, on behalf of the users, evaluate any dynamically chosen computable function on these inputs in the encrypted domain (even across encryptions under different public keys) without learning anything at all, and without interacting with the users.