Secure outsourced skyline query processing via untrusted cloud service providers

Chen, Wenxin; Liu, Mengjun; Zhang, Rui; Zhang, Yanchao; Liu, Shubo

doi:10.1109/infocom.2016.7524509

Cited by 41 publications

(16 citation statements)

References 25 publications

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“…Concerning the privacy of user's dynamic skyline query, three different frameworks were proposed by Chen et al [4], Liu et al [5], and Hua et al [6]. Unfortunately, their secure skyline computation objectives are different from our current scenario.…”

Section: Privacy-preserving Skyline Querymentioning

confidence: 96%

A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

et al. 2019

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Nowadays, the management and analyses of 'big data' are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called 'skyline'. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties' objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties' databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.

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Section: Privacy-preserving Skyline Querymentioning

confidence: 96%

A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

et al. 2019

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“…output: client knows the skyline query result. 1 Compute minimum attribute sum; 2 C 1 : 3 if there is no tuple in E pk (T ) then 4 break; (S (t 1 )), ..., E pk (S (t n ))); 9 Select the skyline with minimum attribute sum; 10 C 1 :…”

Section: Fully Secure Skyline Protocolmentioning

confidence: 99%

Secure and Efficient Skyline Queries on Encrypted Data

Liu

Yang

Xiong

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Furthermore, we demonstrate two optimizations, data partitioning and lazy merging, to further reduce the computation load. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.Manuscript received XXXXXX; revised XXXXXX.

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“…Citation information: DOI 10.1109/JIOT.2018.2834156, IEEE Internet of Things Journal 11 algorithm which used the existing nearest neighbor search to split the data space recursively, while the privacy issue was overlooked. By exploring a novel neighboring relationship among POIs, Chen et al [13] proposed several schemes that enable efficient verification of any location-based skyline query's result returned by an untrusted service provider. In order to select the similar (or best) medical record overencrypted database, Liu et al [14] proposed a fully secure skyline query protocol on data encrypted using semantically secure encryption, while the overhead of computation is heavy.…”

Section: E Settingsmentioning

confidence: 99%

Efficient Emergency Pharmaceutical Service Via Cloud Server

Srilekha¹

2019

IJRASET

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In the past few years it was clear that mobile cloud computing was established via integrating both mobile computing and cloud computing to be add in both storage space and processing speed. These advancements in mobile computing has a potential impact to improve health care delivery, reduce health care costs, make health care services more convenient to patients and increase the overall efficiency and effectiveness of health care providers. The system provides assistance to patients identifies and selects doctors based on the location and the specialties of the doctors. The system allows patients to make appointments with doctors and assigns reminders to take the prescribed medications and vaccinations. The results of testing the applications show a big saving of time and mobility of doctors and patients. The system allows patients to take the prescribed medications and vaccinations. Generally referred to as m-Health, mobile devices are used in conjunction with other information and communication technology facilities to deliver care. A promise of m-Health is its capacity to facilitate the consumption of Electronic Health Record (EHR) data using mobile devices, which is central to promoting remote healthcare delivery. Through mobile technology, physicians are facilitated to interact with patients in a more efficient manner. m-Health employs mobile technology, remote healthcare delivery can be facilitated through patient monitoring, patient data collection, out of health facility patient care, and cost management I.INTRODUCTION Efficient Emergency Pharmaceutical Service via Cloud Server that is used to focuses on supporting patients (persons with tender hemophilia) to self-regulate wounds in occasions of minor scenes. In this Application patients to self-manage injuries in cases of minor incidents. If patients have any query about the minor health problem , they will send the query and get the information about how to manage the injuries. This involves bi-directional exchanges of the Electronic Health Record (EHR) amongst patients and the care facility. In any case, mobile phones rely on wireless communication channels (e.g., 3.5/4G, et cetera.) to transmit data and these channels can experience sporadic disconnections due to bandwidth fluctuations and user mobility. This work took advantage of the ubiquitous nature of mobile cloud computing and proposes a middleware, which facilitates efficient process of medical data synchronization, and with minimal latency. The work details state-of-the-art architecture of the cloud-based middleware that is built and tested for real-world use following four methodologies namely: reflective, tuple space, contextawareness, and event-based.

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Secure outsourced skyline query processing via untrusted cloud service providers

Cited by 41 publications

References 25 publications

A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

Secure and Efficient Skyline Queries on Encrypted Data

Efficient Emergency Pharmaceutical Service Via Cloud Server

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