Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Privacy-Preserving Cloud-Assisted Data Analytics

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users\u27 data may contain private information that needs to be protected. Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics. The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems. To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users. To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user\u27s data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee. The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee. In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures

Secured Data Outsourcing in Cloud Computing

Cloud computing is a popular technology in the IT world. After internet, it is the biggest thing for IT world. Cloud computing uses the Internet for performing the task on the computer and it is the next- generation architecture of IT Industry. It is related to different technologies and the convergence of various technologies has emerged to be called as cloud computing. It places the application software and databases to the huge data centers, where the supervision of the data and services may not be fully trusted. This unique attribute poses many new security challenges which have not been well understood. In this paper, we develop system which allows customer to use cloud server with various profits and strong securities. So when customer stores his sensitive data on cloud server he should not worry about securities, we also protect customer’s account from malicious behaviors by verifying the result. This result verification mechanism is highly efficient for both cloud server and cloud customer. Covering security analysis and experiment results shows the immediate practicability of our mechanism design. DOI: 10.17762/ijritcc2321-8169.150314

Noninteractive Verifiable Outsourcing Algorithm for Bilinear Pairing with Improved Checkability

It is well known that the computation of bilinear pairing is the most expensive operation in pairing-based cryptography. In this paper, we propose a noninteractive verifiable outsourcing algorithm of bilinear pairing based on two servers in the one-malicious model. The outsourcer need not execute any expensive operation, such as scalar multiplication and modular exponentiation. Moreover, the outsourcer could detect any failure with a probability close to 1 if one of the servers misbehaves. Therefore, the proposed algorithm improves checkability and decreases communication cost compared with the previous ones. Finally, we utilize the proposed algorithm as a subroutine to achieve an anonymous identity-based encryption (AIBE) scheme with outsourced decryption and an identity-based signature (IBS) scheme with outsourced verification

Secure Optimization Computation Outsourcing in Cloud Computing: A Case Study of Linear Programming

Abstract-Cloud computing enables an economically promising paradigm of computation outsourcing. However, how to protect customers confidential data processed and generated during the computation is becoming the major security concern. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. Our mechanism design explicitly decomposes LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computation than the general circuit representation. Specifically, by formulating private LP problem as a set of matrices/vectors, we develop efficient privacy-preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP and derive the necessary and sufficient conditions that correct results must satisfy. Such result verification mechanism is very efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design