TY - JOUR
T1 - Break the Data Barriers While Keeping Privacy
T2 - A Graph Differential Privacy Method
AU - Li, Yijing
AU - Tao, Xiaofeng
AU - Zhang, Xuefei
AU - Wang, Mingsi
AU - Wang, Shuo
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers' income. Drivers' satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.
AB - The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers' income. Drivers' satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.
KW - Data ownership management
KW - Internet of Vehicles (IoV)
KW - data sharing
KW - graph differential privacy (DP)
KW - privacy
UR - http://www.scopus.com/inward/record.url?scp=85124811617&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2022.3151348
DO - 10.1109/JIOT.2022.3151348
M3 - Article
AN - SCOPUS:85124811617
SN - 2327-4662
VL - 10
SP - 3840
EP - 3850
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 5
ER -