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Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing

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INFORMATION SCIENCES 2020年第0期518卷 168-180页

作者： Jiang, Zoe L. Guo, Ning Jin, Yabin Lv, Jiazhuo Wu, Yulin Liu, Zechao Fang, Junbin Yiu, S. M. Wang, Xuan Harbin Inst Technol Shenzhen Shenzhen 518055 Peoples R China Jinan Univ Optoelect Engn Guangzhou 510632 Peoples R China Univ Hong Kong Hong Kong 999077 Peoples R China Harbin Engn Univ Harbin 150001 Peoples R China

Nowadays, cloud computing has developed well and been applied in many kinds of areas. However, privacy is still the most challenging problem which obstructs it being applied in some privacy-sensitive fields, such as finance and government. Advanced cryptographic algorithms provide data privacy with encryption, which can also support computation on such encrypted data. However, new challenge arises when such ciphertexts come from different parties. In particular, how to execute collaboratively data mining on encrypted data coming from different parties is a key issue from cloud service point of view. This paper focuses on privacy problem on outsourced k-means clustering scheme for two parties. In particular, each party's data are encrypted only once and then stored in cloud. The proposed privacy-preserving k-means collaborative clustering protocol is executed mainly at the cloud, with O(k(m + n)) rounds of interactions among the two parties and the cloud, where m and n represent the total numbers of records for the two parties, respectively. It is shown that the protocol is secure in the semi-honest security model and in the malicious model in which only one party is corrupted during the process of centroids recomputation. Both theoretical and experimental analysis of the proposed scheme are also provided. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Privacy-preserving data mining Collaborative data mining k-means clustering Storage outsourcing computation outsourcing Secure multi-party computation

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CoOMO: Cost-efficient computation outsourcing with Multi-site Offloading for Mobile-Edge Services 16

CoOMO: Cost-efficient Computation Outsourcing with Multi-sit...

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16th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Meng, Tianhui Wu, Huaming Shang, Zhihao Zhao, Yubin Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tianjin Univ Tianjin Peoples R China Free Univ Berlin Berlin Germany Univ Macau State Key Lab IoTSC Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

ISBN: (纸本)9781728199160

Mobile phones and tablets are becoming the primary platform of choice. However, these systems still suffer from limited battery and computation resources. A popular technique in mobile edge systems is computing outsourcing that augments the capabilities of mobile systems by migrating heavy workloads to resourceful clouds located at the edges of cellular networks. In the multi-site scenario, it is possible for mobile devices to save more time and energy by offloading to several cloud service providers. One of the most important challenges is how to choose servers to offload the jobs. In this paper, we consider a multi-site decision problem. We present a scheme to determine the proper assignment probabilities in a two-site mobile-edge computing system. We propose an open queueing network model for an offloading system with two servers and put forward performance metrics used for evaluating the system. Then in the specific scenario of a mobile chess game, where the data transmission is small but the computation jobs are relatively heavy, we conduct offloading experiments to obtain the model parameters. Given the parameters as arrival rates and service rates, we calculate the optimal probability to assign jobs to offload or locally execute and the optimal probabilities to choose different cloud servers. The analysis results confirm that our multi-site offloading scheme is beneficial in terms of response time and energy usage. In addition, sensitivity analysis has been conducted with respect to the system arrival rate to investigate wider implications of the change of parameter values.

关键词： computation outsourcing Multi-site offloading Mobile-edge computing Queueing networks

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Linearly Homomorphic Signature Based Secure computation outsourcing in Vehicular Adhoc Networks

Linearly Homomorphic Signature Based Secure Computation Outs...

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IEEE International Conference on Innovations in Power and Advanced Computing Technologies

作者： Vasudha Altaf, Fahiem Maity, Soumyadev Indian Inst Informat Technol Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781538681909

In vehicular adhoc network (VANET), periodic messages, known as beacons, are transmitted by vehicles to their neighboring vehicles and surrounding RSU to help them create a logical map of surrounding environment. For ensuring the correctness of this map, beacon messages must be source authenticated to protect the network from bogus and fake messages from attacker vehicles. Further, end user of the beacon messages who performs analysis of beacons would be interested in outsourcing this activity to the third party such as RSU so as to free itself from keeping dedicated computational and bandwidth resources. Therefore, messages should be signed in a manner such that computations performed by RSU are verifiable to the end user without having access to the actual messages and their signature on which computation is performed. In this paper, we implement an efficient linearly homomorphic signature scheme to sign beacon messages such that computations can be performed over them by a third party. The computation results are verifiable to the subscribed users having access to only pseudo identity of the signer vehicle. Our scheme meets the security feature of signature unforgeability and provides required services of authentication and non-repudiation. We manage the user groups by making use of efficient CRT based group key management algorithm and provide vehicles with pseudo identities for privacy preservation. Simulations confirm the efficient nature of our algorithm.

关键词： Vehicular Adhoc Network Linearly Homomorphic Signature Chinese Remainder Theorem computation outsourcing

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Efficient Two-Party Privacy Preserving Collaborative k-means Clustering Protocol Supporting both Storage and computation outsourcing 18th

Efficient Two-Party Privacy Preserving Collaborative <i>k</i...

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18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Jiang, Zoe L. Guo, Ning Jin, Yabin Lv, Jiazhuo Wu, Yulin Yu, Yating Wang, Xuan Yiu, S. M. Fang, Junbin Harbin Inst Technol Shenzhen Shenzhen 518055 Peoples R China Univ Hong Kong Hong Kong Peoples R China Jinan Univ Guangzhou Peoples R China

ISBN: (纸本)9783030050634;9783030050627

Privacy preserving collaborative data mining aims to extract useful knowledge from distributed databases owned by multiple parties while keeping the privacy of both data and mining result. Nowadays, more and more companies reply on cloud to store data and handle with data. In this context, privacy preserving collaborative k-means clustering framework was proposed to support both storage and computation outsourcing for two parties. However, the computing cost and communication overhead are too high to practical. In this paper, we propose to encrypt each party's data once and then store them in cloud. Privacy preserving k-means collaborative clustering protocol is executed mainly at cloud side, with total O(k(m+ n))-round interactions among the two parties and the cloud. Here, m and n means that the total numbers of records for the two parties, respectively. The protocol is secure in the semi-honest security model and especially secure in the malicious model supporting only one party corrupted during k centroids re-computation. We also implement it in real cloud environment using e-health data as the testing data.

关键词： Privacy-preserving data mining k-means clustering Storage outsourcing computation outsourcing Secure multiparty computation

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Cloudroid: A Cloud Framework for Transparent and QoS-aware Robotic computation outsourcing 10

Cloudroid: A Cloud Framework for Transparent and QoS-aware R...

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10th IEEE International Conference on Cloud Computing (CLOUD)

作者： Hu, Ben Wang, Huaimin Zhang, Pengfei Ding, Bo Che, Huimin Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538619933

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re) configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robots capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup.

关键词： Cloud robotics Platform as a service computation outsourcing Quality of service

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Secure Optimization computation outsourcing in Cloud Computing: A Case Study of Linear Programming

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IEEE TRANSACTIONS ON COMPUTERS 2016年第1期65卷 216-229页

作者： Wang, Cong Ren, Kui Wang, Jia City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China SUNY Buffalo Dept Comp Sci & Engn CSE Buffalo NY 14260 USA IIT Dept Elect & Comp Engn Chicago IL 60616 USA

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.

关键词： Confidential data computation outsourcing optimization cloud computing linear programming

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Addition of Fake Variable to Enrich Secure Linear Programming computation outsourcing in the Cloud

Addition of Fake Variable to Enrich Secure Linear Programmin...

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International Conference on Computing, Analytics and Security Trends (CAST)

作者： Ahire, Pallavi Abraham, Jibi Savitribai Phule Pune Univ Dept Technol Pune Maharashtra India Coll Engn Pune Pune Maharashtra India

ISBN: (纸本)9781509013388

Cloud computing plays a vital role in today's IT industry, as it offers tremendous computing and storage facilities for the tasks and outsourced data respectively. The major security concern during the computations is to protect customer's confidential data. The mechanism of this paper decomposes widely applicable linear programming (LP) computation outsourcing into public LP solvers running on a cloud. Efficient privacy preserving problem transformation techniques allows customers to transform original LP into some random LP by keeping private LP parameters and protect input output information. This paper gives detailed mathematical analysis for original LP transformations and LP problem solving. It also proposes a method of adding a fake variable into linear program which will be transformed to random one so that it will be more difficult for an attacker to guess the original problem. The results obtained from the experimentation prove to be efficient and incurs close to zero additional cost.

关键词： Cloud computing computation outsourcing optimization fake variable linear programming

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Efficient computation outsourcing for inverting a class of homomorphic functions

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INFORMATION SCIENCES 2014年 286卷 19-28页

作者： Zhang, Fangguo Ma, Xu Liu, Shengli Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Guangdong Power Grid Corp Informat Ctr Guangzhou 510060 Guangdong Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

The rise of cloud computing and the proliferation of mobile devices make computation outsourcing popular. However, the servers are not fully trusted, and a critical problem is the verifiability and privacy of such computations. Although some computation outsourcing schemes provided a general method, the complicated cryptographic tools involved result in great inefficiency. The existing efficient computation outsourcing schemes however aim only at a specific computation task, lacking in generality. In this paper, we show how to construct a generic outsourcing computation scheme for inverting a class of homomorphic functions with computation disequilibrium. Extensive analysis shows that many cryptographic computations fall into this category. The formal security analysis proves that our scheme satisfies verifiability, input and output privacy in information-theoretic sense. Since the construction of our scheme tactfully takes advantage of the intrinsic property of the computation task being outsourced, no public key operations are used in the scheme, thus our solution clearly outperforms the existing schemes in terms of efficiency. In addition, we instantiate our generic construction with concrete examples, and the experimental result testifies the efficiency of our construction. (C) 2014 Elsevier Inc. All rights reserved.

关键词： computation outsourcing Homomorphic function Security Privacy

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Privacy-preserving and verifiable protocols for scientific computation outsourcing to the cloud

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2014年第3期74卷 2141-2151页

作者： Chen, Fei Xiang, Tao Yang, Yuanyuan Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Chongqing Univ Coll Comp Sci Chongqing 630044 Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

computation outsourcing to the cloud has become a popular application in the age of cloud computing. Recently, two protocols for secure outsourcing scientific computations, i.e., linear equation solving and linear programming solving, to the cloud were proposed. In this paper, we improve the work by proposing new protocols that achieve significant performance gains. For linear equation solving outsourcing, we achieve the improvement by proposing a completely new protocol. The new protocol employs some special linear transformations and there are no homomorphic encryptions and interactions between the client and the cloud, compared with the previous protocol. For linear programming outsourcing, we achieve the improvement by reformulating the linear programming problem in the standard and natural form. We also introduce a method to reduce the key size by using a pseudorandom number generator. The design of the newly proposed protocols also sheds some insight on constructing secure outsourcing protocols for other scientific computations. Comparisons between our protocols and the previous protocols are given, which demonstrate significant improvements of our proposed protocols. We also carry out numerical experiments to validate the efficiency of our protocols for secure linear equation solving and linear programming outsourcing. (C) 2013 Elsevier Inc. All rights reserved.

关键词： computation outsourcing Linear equation solving Linear programming Cloud computing Distributed computing

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How to Securely and Efficiently Solve the Large-Scale Modular System of Linear Equations on the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第3期12卷 913-927页

作者： Tian, Chengliang Yu, Jia Meng, Panpan Zhang, Guoyan Tian, Weizhong Zhang, Yan Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Shandong Peoples R China Shandong Univ Sch Cyber Sci & Technol Key Lab Cryptol Technol & Informat Secur Minist Educ Qingdao 266237 Shandong Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Guangdong Peoples R China Qingdao Univ Sci & Technol Coll Electromech Engn Qingdao 266061 Guangdong Peoples R China

Cloud-assisted computation empowers resource-constrained clients to efficiently tackle computationally intensive tasks by outsourcing them to resource-rich cloud servers. In the current era of Big Data, the widespread need to solve large-scale modular linear systems of equations (LMLSE) of the form Ax equivalent to b mod q poses a significant challenge, particularly for lightweight devices. This paper delves into the secure outsourcing of LMLSE under a malicious single-server model and, to the best of our knowledge, introduces the inaugural protocol tailored to this specific context. The cornerstone of our protocol lies in the innovation of a novel matrix encryption method based on sparse unimodular matrix transformations. This novel technique bestows our protocol with several key advantages. First and foremost, it ensures robust privacy for all computation inputs, encompassing A, b, q, and the output x, as validated by thorough theoretical analysis. Second, the protocol delivers optimal verifiability, enabling clients to detect cloud server misbehavior with an unparalleled probability of 1. Furthermore, it boasts high efficiency, requiring only a single interaction between the client and the cloud server, significantly reducing local-client time costs. For an m-by-n matrix A, a given parameter lambda = omega(log q), and rho=2.371552, the time complexity is diminished from O(max{mn(rho-1),m(rho-2)n(2)}(logq)(2)) to O((mn+m(2))lambda log q + mn(log q)(2)). The comprehensive results of our experimental performance evaluations substantiate the protocol's practical efficiency and effectiveness.

关键词： outsourcing Cloud computing Protocols Mathematical models Servers Task analysis Sparse matrices Client-server system computation outsourcing large-scale linear system of equations privacy-preserving verifiability

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