Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query

Zhang, Lei; Li, Jing; Yang, Songtao; Wang, Bin

doi:10.1007/s11277-017-4299-8

Cited by 23 publications

(17 citation statements)

References 23 publications

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“…To compute the shortest path with obstructions (such as by a traffic jam) in a graph, Zhang et al [289] proposed a secure outsourcing solution using combined cryptographic methods, including MPC, Paillier cryptosystem, and oblivious transfer. In the proposed scheme, the LBS server and cloud server iteratively calculate the shortest path by communicating with each other, based on Floyd-Warshall algorithm.…”

Section: Graph Computationmentioning

confidence: 99%

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A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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Section: Graph Computationmentioning

confidence: 99%

“…The protocols protect the privacy of both the users and the LBS server. However, the computation overhead of the system [289] should be further reduced.…”

Section: Graph Computationmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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“…Since then, other schemes such as the semantic p -sensitivity [11] and the query l -diversity [12] had evolved from the conception of k -anonymity. In recent years, researchers have began to pay close attention to the generalization of location trajectory in continuous query[13, 14], the privacy protection scheme without central server [15–17], as well as the correlation in attributes during the routing of continuous query[18–21],others include the schemes of encryption[22–24].…”

Section: Related Workmentioning

confidence: 99%

A novel ε-sensitive correlation indistinguishable scheme for publishing location data

Wang

Zhang

2019

PLoS ONE

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Nowadays, location based service (LBS) is one of the most popular mobile apps and following with humongous of location data been produced. The publishing of location data can provide benefit for promoting the quality of service, optimizing the commercial environment as well as harmonizing the infrastructure construction. However, as location data may contain some sensitive or confidential information, the publishing may reveal privacy and bring hazards. So the published data had to be disposed to protect the privacy. In order to cope with this problem, a number of algorithms based on the strategy of k-anonymity were proposed, but this is not enough for the privacy protection, as the correlation between the sensitive region and the background knowledge can be used to infer the real location. Thus, consider about this condition, in this paper a ε-sensitive correlation privacy protection scheme is proposed, and provides correlation indistinguishable to the location data. In this scheme, entropy is first used to determine the location centroid of each cell to build up the voronoi diagram. Then the coordinate of the untreated location data that is located in the cell is transferred into the centroid vicinity. Accordingly, the sensitive correlation is destroyed by the coordinate of each published data. The process of transferring the location data is determined by metrics of ε-sensitive correlation privacy, and is rigorous in mathematical justification. At last, security analysis is proposed in this paper to verify the privacy ability of our proposed algorithm based on voronoi diagram and entropy, and then we utilize the comparative experiment to further affirm the advantage of this algorithm in the location data privacy protection as well as the availability of published data.

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“…In this algorithm, the CS is just used to expand the anonymous range, and the anonymous user with similar attribute is selected by the ability of decryption. Similar to above algorithm, Zhang et al [ 21 ] completed the private shortest path calculation without any attribute to be observed in cloud environment with the help of homomorphic encryption.…”

Section: Introductionmentioning

confidence: 99%

Walking along the road with anonymous users in similar attributes

Bian¹,

Zhang²,

Yu³

et al. 2018

PLoS ONE

Self Cite

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Recently, the ubiquitousness of smartphones and tablet computers have changed the style of people’s daily life. With this tendency, location based service (LBS) has become one of the prosperous types of service along with the wireless and positioning technology development. However, as the LBS server needs precise location information about the user to provide service result, the procedure of LBS may reveal location privacy, especially when a user is utilizing continuous query along the road. In continuous query, attributes of the user are released inadvertently with per-query, and the information can be collected by an adversary as background knowledge to correlate the location trajectory and infer the personal privacy. Although, a user can employ a central server (CS) to provide privacy preservation for his location, the trustfulness of CS still is without testified and it is usually considered as an un-trusted entity. Thus, in this paper, the trustfulness of CS is verified by a game tree, and then with the result we propose a hash based attribute anonymous scheme (short for HBAA) to obfuscate the attributes released in each query along the road. With the help of HBAA, the CS has no opportunity to get any information about the user who sends his query for generalization service. Furthermore, as the set of attributes is transmitted into a fixed length of hash value, the processing time that spent in attribute generalization is stripped down and the performance of executive efficiency is improved. At last, security analysis and simulation experiment are proposed, and then results of security proving as well as simulation experiments further reflect the superiority of our proposed scheme.

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Privacy Preserving in Cloud Environment for Obstructed Shortest Path Query

Cited by 23 publications

References 23 publications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A novel ε-sensitive correlation indistinguishable scheme for publishing location data

Walking along the road with anonymous users in similar attributes

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