Privacy-Preserving Distributed Service Recommendation Based on Locality-Sensitive Hashing

Abstract

This version is available at http://eprints.hud.ac.uk/id/eprint/31929/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way.For more information, including our policy and submission procedure, please contact the Repository Abstract-With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users' service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSR LSH is put forward in this paper. Through LSH, DistSR LSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems.

Privacy-aware cross-cloud service recommendations based on Boolean historical invocation records

“…We extract the Boolean (user, service) pairs from these QoS data for experiment purpose. To show the advantages of our solution, we compare Rec MPS method with three state-of-the-art methods, e.g., DistSR LSH [27], WSRec [28], and ICF (item-based CF). We compare the performances of the four methods in terms of recommendation accuracy (via RMSE) and efficiency.…”

NTRU Implementation of Efficient Privacy-Preserving Location-Based Querying in VANET

“…However, in order to ensure the functionality of the QSP, it is impossible to find an all-sided protection scheme purely based on coordinate transformation because the service provider has to be able to determine the relative position of objects and areas to each other [17]. In addition, to preserve the trajectory of user, a great deal of spatiotemporal location obfuscation schemes are also proposed, which also took the temporal information associated with positions into account [18][19][20][21][22][23][24][25]. [26] who split up the location information into shares of strictly limited precision.…”

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

“…However, the work in [12] fails to protect some important privacy information appropriately, e.g., the information of the service intersection commonly invoked by two users. A privacy-preserving recommendation approach is proposed in our previous work [16]; however, this approach cannot handle the service recommendation scenario where the service quality data observed by a user are distributed in multiple platforms.…”

“…Due to its domain-independent and easy-to-explain characteristics, collaborative filtering (CF) has become one of the most effective techniques in various service recommendation systems [4]- [16]. We briefly review the CF-based service recommendation approaches from three perspectives: recommendation accuracy, capability of privacy preservation and scalability.…”