Format: YANG H Y, ZHANG H M, ZHU Y W, et al. Differential privacy protection method for localization of ADS-B message positioning data oriented to LBS[J]. Chinese Journal of Network and Information Security, 2025, 11(4): 32-49.
Format: YANG H Y, ZHANG H M, ZHU Y W, et al. Differential privacy protection method for localization of ADS-B message positioning data oriented to LBS[J]. Chinese Journal of Network and Information Security, 2025, 11(4): 32-49. DOI: 10.11959/j.issn.2096-109x.2025041.
Differential privacy protection method for localization of ADS-B message positioning data oriented to LBS
Due to the completely open format of automatic dependent surveillance-broadcast (ADS-B) messages and the lack of encryption for critical data
sensitive location information was put at risk of exposure. To address the privacy leakage issues caused by untrusted third-party Location-Based Services during the collection and publication of ADS-B message location data
a phased equal-step randomized response privacy protection method was proposed. Dimensionality reduction was performed on the three-dimensional location data
and a one-dimensional octal location information code was generated through binary crossover encoding. A phased equal difference randomized response method was employed to perturb the one-dimensional location information code. The concept of equal difference perturbation was utilized to create ladder-like groupings in the randomized response space. Subsequently
the randomized response process was divided into two phases: a random response based on equal difference privacy
and an adaptive random response space partitioned by equal difference probability
thereby enhancing data privacy throughout the randomization process. Finally
a phase-based probabilistic weighted Kalman filter estimation method was proposed to estimate the frequency of the privacy data after randomization. The privacy and feasibility of this approach were demonstrated through theoretical analysis. Experimental results indicate that the proposed method effectively protects the privacy of ADS-B sensitive location data while significantly reducing the error between the frequency statistics of the privacy-protected data and the original data
thus ensuring data validity.
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references
AHMED H , KHAN H , KHAN M A . A survey on security and privacy of automatic dependent surveillance-broadcast (ADS-B) protocol: Challenges, potential solutions and future directions [J ] . Authorea Preprints , 2023 .
DING J L , ZOU Y K , WANG J , et al . ADS-B anomaly data detection model based on deep learning [J ] . ACTA AERONAUTICAET ASTRONAUTICA SINICA , 2019 , 40 ( 12 ): 323220 - 323220 .
LI H T , REN X Y , WANG J , et al . Continuous location privacy protection mechanism based on differential privacy [J ] . Journal on Communications , 2021 , 42 ( 8 ): 164 - 175 .
CHEN S , FU A M , SU M , et al . Trajectory privacy protection scheme based on differential privacy [J ] . Journal on Communications , 2021 , 42 ( 9 ): 54 - 64 .
SUN X Y , ZHANG W Z , HE H , et al . Survey of differentially private methods for trajectory data [J ] . Chinese Journal of Network and Information Security , 2025 , 11 ( 3 ): 1 - 18 .
ARCOLEZI H H , COUCHOT J F , AL BOUNA B , et al . Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates [J ] . Digital Communications and Networks , 2024 , 10 ( 2 ): 369 - 379 .
WANG H , KONG Y , HONG Y , et al . Publishing video data with indistinguishable objects [C ] //Advances in Database Technology: Proceedings . International Conference on Extending Database Technology . 2020 : 323 .
ERLINGSSON Ú , PIHUR V , KOROLOVA A . Rappor: Randomized aggregatable privacy-preserving ordinal response [C ] // Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security . 2014 : 1054 - 1067 .
KAIROUZ P , BONAWITZ K , RAMAGE D . Discrete distribution estimation under local privacy [C ] // International Conference on Machine Learning . PMLR , 2016 : 2436 - 2444 .
WANG T , BLOCKI J , LI N , et al . Locally differentially private protocols for frequency estimation [C ] // 26th USENIX Security Symposium (USENIX Security 17). 2017 : 729 - 745 .
ACHARYA J , SUN Z , ZHANG H . Hadamard response: estimating distributions privately, efficiently, and with little communication [C ] // The 22nd International Conference on Artificial Intelligence and Statistics . PMLR , 2019 : 1120 - 1129 .
CHEN R , LI H , QIN A K , et al . Private spatial data aggregation in the local setting [C ] // 2016 Piscataway: IEEE Press 32nd International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2016 : 289 - 300 .
FANG H , CHEN L , LIU Y , et al . Locally differentially private frequency estimation based on convolution framework [C ] // 2023 IEEE Symposium on Security and Privacy (SP) . Piscataway : IEEE Press , 2023 : 2208 - 2222 .
KIM J W , JANG B . Workload-aware indoor positioning data collection via local differential privacy [J ] . IEEE Communications Letters , 2019 , 23 ( 8 ): 1352 - 1356 .
ZHAO X , LI Y , YUAN Y , et al . LDPart: effective location-record data publication via local differential privacy [J ] . IEEE Access , 2019 , 7 : 31435 - 31445 .
WANG H , HONG H , XIONG L , et al . L-srr: local differential privacy for location-based services with staircase randomized response [C ] // Proceedings of the 2022 ACM SIGSAC Conference on Computer And Communications Security . 2022 : 2809 - 2823 .
WANG T , ZHANG X , FENG J , et al . A comprehensive survey on local differential privacy toward data statistics and analysis [J ] . Sensors , 2020 , 20 ( 24 ): 7030 .
LI L , LU R , HUANG C . EPLQ: efficient privacy-preserving location-based query over outsourced encrypted data [J ] . IEEE Internet of Things Journal , 2015 , 3 ( 2 ): 206 - 218 .
ASARI A , ALAGHEBAND M R , BAYAT M , et al . A new provable hierarchical anonymous certificateless authentication protocol with aggregate verification in ADS-B systems [J ] . Computer Networks , 2021 , 185 : 107599 .
Ministry of Natural Resources of the People's Republic of China . Geospatial grid code rules : GB/T 40087-2021 [S ] . Beijing : Standardization Administration of China , 2021 .