Toward Privacy-Preserving Aggregate Reverse Skyline Query With Strong Security

Zhang, Songnian; Ray, Suprio; Lu, Rongxing; Guan, Yunguo; Zheng, Yandong; Shao, Jun

doi:10.1109/tifs.2022.3188147

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…The size of the two datasets in the experiment was 1M. The dimensionality was varied in the interval [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The CPU execution times of BTIS, SIDS, Sky-iDS and ∆skyline vary with increasing data dimensionality, as shown in Figures 8 and 9: 8 and 9, as the horizontal coordinate, i.e., the dimension, increases, the CPU running times of the four algorithms level off, and the time difference between the horizontal coordinate interval 3 and 11 is small.…”

Section: A Comparative Analysis Of Algorithmsmentioning

confidence: 99%

“…The size of the two datasets in the experiments is 1M. The dimensionality of the data varies in the interval [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The variation of accuracy of BTIS, ∆skyline, SIDS and Sky-iDS algorithms in the experiments is shown in Fig.…”

Section: A Comparative Analysis Of Algorithmsmentioning

confidence: 99%

“…Currently, the skyline query technology studied at home and abroad mainly aims at the skyline query in the complete data environment. For example, probabilistic skyline query [1], skyline query for massive data [2], skyline query for mobile edge computing [3], skyline query technology in obstacle space [4], cluster design of spatial database [5], LSM index storage for databases [6], kdominant skyline query algorithm for dynamic data sets [7], skyline query based on privacy protection [8] and K-dominant skyline query [9]. The technology of skyline query is constantly improving.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

skyline query under multidimensional incomplete data based on classification tree

Yuan,

Zhang,

et al. 2024

Preprint

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A method for skyline query of multidimensional incomplete data based on a classification tree has been proposed to address the problem of a large amount of useless data in existing skyline queries with multi-dimensional incomplete data, which leads to low query efficiency and algorithm performance. This method consists of two main parts. The first part is the proposed incomplete data weighted classification tree algorithm. In the first part, an incomplete data weighted classification tree is proposed, and the incomplete data set is classified using this tree. The data classified in the first part serves as the basis for the second step of the query. The second part proposes a skyline query algorithm for multi-dimensional incomplete data. The concept of optimal virtual points has been recently introduced, effectively reducing the number of comparisons of a large amount of data, thereby improving the query efficiency for incomplete data. Theoretical research and experimental analysis have shown that the proposed method can perform skyline queries for multi-dimensional incomplete data well, with high query efficiency and accuracy of the algorithm.

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Section: A Comparative Analysis Of Algorithmsmentioning

confidence: 99%

Section: A Comparative Analysis Of Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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skyline query under multidimensional incomplete data based on classification tree

Yuan,

Zhang,

et al. 2024

Preprint

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show abstract

“…The feasibility of patients using public LLM platforms to discuss health conditions without compromising privacy remains problematic. Even without disclosing identifiable information, research has demonstrated the potential for deducing user identities through aggregated web search queries (Hussien et al 2013 ; Zhang et al 2022 ). One proposed solution is the deployment of private, locally hosted LLMs, which, while addressing privacy concerns, introduces significant costs associated with maintaining the necessary computational infrastructure (Hong et al 2023 ).…”

Section: Bridging the Gap With Llmsmentioning

confidence: 99%

Large language models as decision aids in neuro-oncology: a review of shared decision-making applications

Lawson McLean,

Wu,

Lawson McLean

et al. 2024

J Cancer Res Clin Oncol

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Shared decision-making (SDM) is crucial in neuro-oncology, fostering collaborations between patients and healthcare professionals to navigate treatment options. However, the complexity of neuro-oncological conditions and the cognitive and emotional burdens on patients present significant barriers to achieving effective SDM. This discussion explores the potential of large language models (LLMs) such as OpenAI's ChatGPT and Google's Bard to overcome these barriers, offering a means to enhance patient understanding and engagement in their care. LLMs, by providing accessible, personalized information, could support but not supplant the critical insights of healthcare professionals. The hypothesis suggests that patients, better informed through LLMs, may participate more actively in their treatment choices. Integrating LLMs into neuro-oncology requires navigating ethical considerations, including safeguarding patient data and ensuring informed consent, alongside the judicious use of AI technologies. Future efforts should focus on establishing ethical guidelines, adapting healthcare workflows, promoting patient-oriented research, and developing training programs for clinicians on the use of LLMs. Continuous evaluation of LLM applications will be vital to maintain their effectiveness and alignment with patient needs. Ultimately, this exploration contends that the thoughtful integration of LLMs into SDM processes could significantly enhance patient involvement and strengthen the patient-physician relationship in neuro-oncology care.

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“…Unfortunately, SSE fails to support complex query functions such as geometric range queries and lacks flexible key management for multi-user task allocation scenarios. In particular, some works have explored schemes for k-Nearest Neighbor (kNN) queries [15] and skyline queries [16] with privacy guarantees. However, these schemes cannot be directly applied to geometric range queries.…”

Section: Introductionmentioning

confidence: 99%

Enabling Efficient and Privacy-Preserving Task Allocation with Temporal Access Control for Mobile Crowdsensing

Song

Liu²,

et al. 2023

Electronics

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The increasing proliferation of GPS-enabled mobile devices, including Unmanned Aerial Vehicles (UAVs), smartphones, and laptops, has resulted in a significant upsurge in the outsourcing of spatial data to cloud servers for storage and computation purposes, such as task allocation and location-based services. However, the reliance on untrusted cloud servers introduces the risk of privacy breaches, as these servers possess the ability to deduce and access users’ private information based on task content and query requirements. Existing privacy-preserving task-allocation schemes offer only coarse-grained and non-temporal access control, which restricts their applicability in scenarios involving multiple users and time-series data, such as trajectory and time-related routes. To overcome these challenges, this paper proposes an Efficient and Privacy-Preserving Task Allocation with Temporal Access Control (EPTA-T) scheme for mobile crowdsensing. By leveraging the techniques of Gray code and randomizable matrix multiplication, EPTA-T achieves efficient and privacy-preserving task allocation in mobile crowdsensing. Specifically, EPTA-T supports fine-grained and temporal access control through the utilization of an attribute-based access tree and function integration. The formal security analysis demonstrated that EPTA-T effectively guarantees data privacy and query privacy throughout the task allocation process. Extensive experiments conducted using a real-world dataset indicated that the EPTA-T scheme surpassed the performance of the state-of-the-art scheme.

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Toward Privacy-Preserving Aggregate Reverse Skyline Query With Strong Security

Cited by 12 publications

References 32 publications

skyline query under multidimensional incomplete data based on classification tree

skyline query under multidimensional incomplete data based on classification tree

Large language models as decision aids in neuro-oncology: a review of shared decision-making applications

Enabling Efficient and Privacy-Preserving Task Allocation with Temporal Access Control for Mobile Crowdsensing

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