Privacy preserving online matching on ridesharing platforms

Xu, Yi; Wei, Shuyue; Wang, Yansheng

doi:10.1016/j.neucom.2019.09.116

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Tong et al [35] conduct the experimental study on online bipartite matching and show that the greedy algorithm is competitive in many practical settings. Later studies explore task assignment with different objectives or constraints, such as fairness [23], [36], [37], privacy [38]- [40] and incentive [41]- [43]. Particularly, the fair-aware task assignment algorithm in [23] can be extended as a baseline to avoid the overloaded phenomenon in our scenario.…”

Section: Related Workmentioning

confidence: 99%

Towards Capacity-Aware Broker Matching: From Recommendation to Assignment

Wei¹,

Tong²,

Zhou³

et al. 2023

Preprint

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Online real estate platforms are gaining increasing popularity, where a central issue is to match brokers with clients for potential housing transactions. Mainstream platforms match brokers via top-k recommendation. Yet we observe through extensive data analysis that such top-k recommendation tends to overload the top brokers, which notably degrades their service quality. In this paper, we propose to avoid such overloading in broker matching via the paradigm shift from recommendation to assignment. To this end, we design learned assignment with contextual bandits (LACB), a data-driven capacity-aware assignment scheme for broker matching which estimates broker-specific workload capacity in an online fashion and assigns brokers to clients from a global perspective to maximize the overall service quality. Extensive evaluations on synthetic and real-world datasets from an industrial online real estate platform validate the efficiency and effectiveness of our solution.

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Section: Related Workmentioning

confidence: 99%

Towards Capacity-Aware Broker Matching: From Recommendation to Assignment

Wei¹,

Tong²,

Zhou³

et al. 2023

Preprint

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“…Based on its good security, serval location privacy protection mechanisms have been proposed. Xu et al [11] propose a framework based on geo-indistinguishability to preserve the privacy of individuals on ridesharing platforms. Tao et al [12] investigate privacy protection for online task assignment with the objective of minimizing the total distance.…”

Section: Related Workmentioning

confidence: 99%

Regionalized location obfuscation mechanism with personalized privacy levels

Zhang¹,

Lan²,

Duan³

et al. 2021

Preprint

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Global Positioning Systems are now a standard module in mobile devices, and their ubiquity is fueling rapid growth of location-based services (LBSs). This poses the risk of location privacy disclosure. Effective location privacy preservation is foremost for various mobile applications. Recently two strong privacy notions, geo-indistinguishability and expected inference error, are proposed based on statistical quantification. They are complementary for limiting the leakage of location information. In this paper, we continue to study the differential privacy preservation of location obfuscation mechanism based on PIVE framework proposed by Yu, Liu and Pu on ISOC Network and Distributed System Security Symposium (NDSS) in 2017. Since PIVE fails to offer differential privacy guarantees on adaptive protection location set (PLS) as claimed, we develop DPIVE, a regionalized location obfuscation mechanism with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the common PLS. In Phase II, we construct a probability distribution matrix by exponential mechanism in which each row has its own sensitivity of utility (diameter of PLS).This approach utilizes the relationship between two privacy notions based on the user-defined inference error threshold and the prior knowledge about user's location. Moreover, we introduce PDPIVE, a personalized privacy framework, to achieve that each location has its own privacy level on two privacy control knobs, minimum inference error and differential privacy parameter. Experiments with two public datasets demonstrate that our mechanisms have the superior performance typically on skewed locations.

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“…For instance, the Waze application suggests drivers suitable routes to help drivers to avoid congestion. This kind of service benefits users' daily life, but it may raise privacy concerns of sensitive data such as users' health data in the wearable devices and users' location information [24][25][26][27]. Besides, as the number of IoT devices increases, IoT smart devices usually generate tremendous data.…”

Section: Background Of Iotmentioning

confidence: 99%

Privacy-preserving data analytics

Zhao¹

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whose continuous guidance, support, and encouragement have been invaluable throughout this study. Under their close mentorship, I have successfully completed a number of publications. I have also come to know how to explore new research areas, choose research topics, build new approaches and do experiments. In particular, their enthusiasm and awareness of research have encouraged me to do better successes. Furthermore, I would like to express my thankfulness to my thesis advisory committee members Prof. NG Wee Keong, Asst Prof. Hung Dinh Nguyen, and Asst Prof. Han Yu, for their valuable comments on my research.

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Privacy preserving online matching on ridesharing platforms

Cited by 9 publications

References 9 publications

Towards Capacity-Aware Broker Matching: From Recommendation to Assignment

Towards Capacity-Aware Broker Matching: From Recommendation to Assignment

Regionalized location obfuscation mechanism with personalized privacy levels

Privacy-preserving data analytics

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