TY - GEN
T1 - A privacy design problem for sharing transport service tour data
AU - He, Yueshuai
AU - Chow, Joseph Y.J.
AU - Nourinejad, Mehdi
N1 - Funding Information:
ACKNOWLEDGMENTS The authors are partially supported by the NSF CAREER grant CMMI-1652735, which is gratefully acknowledged.
Funding Information:
The authors are partially supported by the NSF CAREER grant CMMI-1652735, which is gratefully acknowledged.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/14
Y1 - 2018/3/14
N2 - Despite the increasing relevance of private transport operators as Mobility-as-a-Service in the success of smart cities, desire for privacy in data sharing limits collaborations with public agencies. We propose an original model that circumvents this limitation, by designing a diffusion of the data - in this case, service tour data - such that passenger travel times remain reliable to the recipient agency. The Tour Sharing Privacy Design Problem is formulated as a nonlinear programming problem that maximizes entropy. We investigate properties of the model and iterative tour generation algorithms, and conduct a series of numerical experiments on an instance that has 90 feasible tours. The experimental results show that a k-best shortest tour approach of generating tours iteratively initially increases the gap to a lower bound before decreasing toward a final constraint gap. The model is shown to recognize the trade-offs between more reliability in data and more anonymity. Comparisons between the true and diffused travel times and OD matrices are made.
AB - Despite the increasing relevance of private transport operators as Mobility-as-a-Service in the success of smart cities, desire for privacy in data sharing limits collaborations with public agencies. We propose an original model that circumvents this limitation, by designing a diffusion of the data - in this case, service tour data - such that passenger travel times remain reliable to the recipient agency. The Tour Sharing Privacy Design Problem is formulated as a nonlinear programming problem that maximizes entropy. We investigate properties of the model and iterative tour generation algorithms, and conduct a series of numerical experiments on an instance that has 90 feasible tours. The experimental results show that a k-best shortest tour approach of generating tours iteratively initially increases the gap to a lower bound before decreasing toward a final constraint gap. The model is shown to recognize the trade-offs between more reliability in data and more anonymity. Comparisons between the true and diffused travel times and OD matrices are made.
KW - Mobility-as-a-Service
KW - Public-Private Partnership
KW - Shannon entropy
KW - k-best traveling salesman problem
KW - privacy design problem
KW - vehicle tours
UR - http://www.scopus.com/inward/record.url?scp=85046277403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046277403&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2017.8317692
DO - 10.1109/ITSC.2017.8317692
M3 - Conference contribution
AN - SCOPUS:85046277403
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1
EP - 6
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -