Secure outsourcing of some computations

Cloud computing enables customers with limited computational resources to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner. However, security is the major concern that prevents the wide adoption of computation outsourcing in the cloud, especially when end-user's confidential data are processed and produced during the computation. Thus, secure outsourcing mechanisms are in great need to not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem.

Lecture Notes in Computer Science, 2015

Secure multiparty computation (SMC) offers a technique to preserve functionality and data privacy in mobile applications. Current protocols that make this costly cryptographic construction feasible on mobile devices securely outsource the bulk of the computation to a cloud provider. However, these outsourcing techniques are built on specific secure computation assumptions and tools, and applying new SMC ideas to the outsourced setting requires the protocols to be completely rebuilt and proven secure. In this work, we develop a generic technique for lifting any secure two-party computation protocol into an outsourced two-party SMC protocol. By augmenting the function being evaluated with auxiliary consistency checks and input values, we can create an outsourced protocol with low overhead cost. Our implementation and evaluation show that in the best case, our outsourcing additions execute within the confidence intervals of two servers running the same computation, and consume approximately the same bandwidth. In addition, the mobile device itself uses minimal bandwidth over a single round of communication. This work demonstrates that efficient outsourcing is possible with any underlying SMC scheme, and provides an outsourcing protocol that is efficient and directly applicable to current and future SMC techniques.

International Journal of Information Security, 2015

Reducing computational cost of cryptographic computations for resource-constrained devices is an active research area. One of the practical solutions is to securely outsource the computations to an external and more powerful cloud server. Modular exponentiations are the most expensive computation from the cryptographic point of view. Therefore, outsourcing modular exponentiations to a single, external and potentially untrusted cloud server while ensuring the security and privacy provide an efficient solution. In this paper, we propose new efficient outsourcing algorithms for modular exponentiations using only one untrusted cloud server. These algorithms cover public-base & private-exponent, private-base & public-exponent, private-base & privateexponent, and more generally private-base & private-exponents simultaneous modular exponentiations. Our algorithms are the most efficient solutions utilizing only one single untrusted server with best checkability probabilities. Furthermore, unlike existing schemes, which have fixed checkability probability, our algorithms provide adjustable predetermined checkability parameters. Finally, we apply our algorithms to outsource Oblivious Transfer Protocols and Blind Signatures which are expensive primitives in modern cryptography.

2020

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of r...

Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016

The massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.

Abstract We present a general-purpose protocol that enables a client to delegate the computation of any function to a cluster of n machines in such a way that no adversary that corrupts at most n− 1 machines can recover any information about the client's input or output. The protocol makes black-box use of multi-party computation (MPC) and secret sharing and inherits the security properties of the underlying MPC protocol (ie, passive vs. adaptive security and security in the presence of a semi-honest vs. malicious adversary).

Cloud computing can be seen as an innovation in different ways. From a technological perspective it is an advancement of computing, which's history can be traced back to the construction of the calculating machine. Cloud Computing has great potential of providing robust computational power to the society at reduced cost. It enables customers with limited computational resources to outsource their large computation workloads to the cloud, and economically enjoy the massive computational po wer, bandwidth, storage, and even appropriate software that can be shared in a pay-per-use manner. We must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect custome rs from malicious behaviors by enabling the validation of the computation result. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allo ws us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output info rmation. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers.

2013

We study interactive algorithmic schemes for outsourcing matrix computations on untrusted global computing infrastructures such as clouds or volunteer peer-to-peer platforms. In these schemes the client outsources part of the computation with guaranties on both the inputs' secrecy and output's integrity. For the sake of efficiency, thanks to interaction, the number of operations performed by the client is almost linear in the input/output size, while the number of outsourced operations is of the order of matrix multiplication. Our scheme is based on efficient linear codes (especially evaluation/interpolation version of Reed-Solomon codes). Confidentiality is ensured by encoding the inputs using a secret generator matrix, while fault tolerance is ensured together by using fast probabilistic verification and high correction capability of the code. The scheme can tolerate multiple malicious errors and hence provides an efficient solution beyond resilience against soft errors. T...

2019

There are currently major concerns about how to safeguard and process the data processed by infection. Innumerable industrial, figuring and optimization methods are being used to resolve this problem. The problem has been fixed for secure outsourcing for large issues. In this paper, the terms required in Cloud Security have been presented. The privacy feats of secure cloud are used to frustrate, to achieve more aspects of security. While cloud computing is being used to outsource large scale computer outsourced to the cloud, data privacy has become a major problem. In this paper, modern cryptographic techniques, which have been sourcing with research work proposed in the previous years. Based on some flaw fixes, the current situation has been identified. There is also the motivation for this paper problem and future research guidelines.