Differentially Private Tripartite Intelligent Matching Against Inference Attacks in Ride-Sharing Services

He, Yanlin; Ni, Jianbing; Yang, Laurence T.; Wei, Wei; Deng, Xianjun; Zou, Deqing; Shah, Syed Hassan Ahmed

doi:10.1109/tits.2021.3136386

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…In ride-sharing services, the trichromatic matching prob- lem received attention in [12] and [13]. The former examined multi-capacity ride-sharing and presented a competitive analysis of matching resources, requests, and request groups.…”

Section: B Online Trichromatic Matchingmentioning

confidence: 99%

Randomized ϵ-RANKING Algorithm for Online Trichromatic Matching

Pandya,

Maiti

2024

IEEE Access

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We study a randomized 1 e -competitive ϵ-RANKING algorithm for the online trichromatic matching problem. This problem involves finding matching in tripartite graphs having three disjoint sets of vertices. Our model encompasses a tripartite graph configuration, where an offline vertex set is an intermediary connecting two opposite sets. The vertices from the latter sets arrive online individually over time. ϵ-RANKING employs a randomization strategy where offline vertices are assigned random ranks between [0, 1]. This approach exploits the principles of the 1 − 1 e -competitive randomized RANKING algorithm from the domain of online bipartite matching to the more complex context of online trichromatic matching. Furthermore, with numerical experiments, we demonstrate the effectiveness of our algorithm, both with real-world and synthetic data.

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Section: B Online Trichromatic Matchingmentioning

confidence: 99%

Randomized ϵ-RANKING Algorithm for Online Trichromatic Matching

Pandya,

Maiti

2024

IEEE Access

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“…In ride-sharing services, the trichromatic matching problem received attention from Lowalekar et al [12] and He et al [13]. The former examined multi-capacity ridesharing and presented a competitive analysis of matching resources, requests, and request groups.…”

Section: Online Trichromatic Matchingmentioning

confidence: 99%

Randomized ε-RANKING Algorithm for Online Trichromatic Matching

Pandya,

Maiti

2023

Preprint

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We study a randomized 1/e-competitive ε-RANKING algorithm for the onlinetrichromatic matching problem. This problem involves finding matching in tripartite graphs having three disjoint sets of vertices. Our model encompasses a tripartite graph configuration, where an offline vertex set is an intermediary connecting two opposing sets. The vertices from the latter sets unfold online, arriving individually over time. ε-RANKING employs a randomization strategy where offline vertices are assigned random ranks between [0, 1]. It extends the idea of finding the lowest-ranked neighbor of the newly arrived online vertex presented by Karp et al. [1] in their RANKING algorithm. This approach exploits the principles of RANKING and matching from the domain of online bipartite matching to the more complex context of online trichromatic matching.

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