SPFM: Scalable and Privacy-Preserving Friend Matching in Mobile Cloud

Li, Mengyuan; Ruan, Na; Qian, Qiyang; Zhu, Haojin; Liang, Xiaohui; Yu, Le

doi:10.1109/jiot.2016.2582780

Cited by 25 publications

(16 citation statements)

References 27 publications

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“…Specifically, from the perspective of cryptography, they presented insights into the potential privacy issues which may appear in user operations. Li et al [79] studied the privacy issues which may emerge in profile matching, which was quite significant in different social network applications. They presented a scalable protection scheme which prevented personal information from leakage in the mobile cloud system.…”

Section: Security and Privacy Preserving For Mobile Cloud Healthcarementioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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Mobile devices, along with wearable sensors, allow patients to access healthcare services from anywhere at any time. The longstanding constraints of computational capability and storage space on mobile devices can be alleviated by outsourcing computation-or data-intensive tasks to remote cloud centers. Thus, mobile cloud computing (MCC) has been recognized as a promising approach to provide pervasive healthcare services to people in their daily life. As the development and adoption of MCC techniques in healthcare, new optimization strategies have been explored and studied to help mobile cloud healthcare services to be deployed in a more effective and efficient manner. In this survey, we demonstrate how MCC techniques have been extensively deployed in various healthcare applications and, specifically, describe the general architecture and design considerations one should take into account while designing an MCC for healthcare scenarios. Given a large number of factors that may affect the performance of the MCC and even result in catastrophic consequences in healthcare, this paper presents the state-of-the-art optimization methods on the MCC for meeting the diverse priorities and achieving the optimal tradeoff among multiple objectives. Finally, the security and privacy issues of the MCC in healthcare are also discussed. INDEX TERMS Mobile cloud computing, pervasive healthcare. optimization, offloading, security and privacy. NOMENCLATURE

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Section: Security and Privacy Preserving For Mobile Cloud Healthcarementioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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“…For example, consider friend matching (or friend recommendation) based on personal data (e.g., locations, rating history) [10,14,16,35,36,38,44,47]. In the case of locations, we can create a vector of visit-counts where each value is the visit-count on the corresponding Point of Interest (POI).…”

Section: Introductionmentioning

confidence: 99%

Locality Sensitive Hashing with Extended Differential Privacy

Fernandes

Kawamoto

Murakami

2021

Lecture Notes in Computer Science

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Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility. However, existing works on extended DP are limited to few metrics, such as the Euclidean metric. Consequently, they have only a small number of applications, such as location-based services and document processing. In this paper, we propose a couple of mechanisms providing extended DP with a different metric: angular distance (or cosine distance). Our mechanisms are based on locality sensitive hashing (LSH), which can be applied to the angular distance and work well for personal data in a high-dimensional space. We theoretically analyze the privacy properties of our mechanisms, and prove extended DP for input data by taking into account that LSH preserves the original metric only approximately. We apply our mechanisms to friend matching based on high-dimensional personal data with angular distance in the local model, and evaluate our mechanisms using two real datasets. We show that LDP requires a very large privacy budget and that RAPPOR does not work in this application. Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.

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“…A basic LBS is location proximity detection that enables a user to test whether another user is nearby. This promising function has boosted the development of social applications to help users to find their nearby friends [1], Uber cars [2], or medical personnel in an event of emergency [3]. Although some users have nothing against sharing their location, many privacy-aware users want to protect it from third parties.…”

Section: Introductionmentioning

confidence: 99%