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Improve symmetry of arbiter in APUF
A real-time aggregate data publishing scheme with adaptive ω-event differential privacy
1. | School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China |
2. | School of Big Data & Software Engineering, Chongqing University, Chongqing, China |
Although massive real-time data collected from users can provide benefits to improve the quality of human daily lives, it is possible to expose users' privacy. $\epsilon$-differential privacy is a notable model to provide strong privacy preserving in statistics. The existing works highlight $ω$-event differential privacy with a fixed window size, which may not be suitable for many practical scenarios. In view of this issue, we explore a real-time scheme with adaptive $ω$-event for differentially private time-series publishing (ADP) in this paper. In specific, we define a novel notion, Quality of Privacy (QoP) to measure both the utility of the released statistics and the performance of privacy preserving. According to this, we present an adaptive $ω$-event differential privacy model that can provide privacy protection with higher accuracy and better privacy protection effect. In addition, we also design a smart grouping mechanism to improve the grouping performance, and then improve the availability of publishing statistics. Finally, comparing with the existing schemes, we exploit real-world and synthetic datasets to conduct several experiments to demonstrate the superior performance of the ADP scheme.
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Publishing setvalued data via differential privacy, Vldb, 4 (2012), 1087-1098.
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Extracting kernel dataset from big sensory data in wireless sensor networks, IEEE Transactions on Knowledge and Data Engineering, 29 (2017), 813-827.
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Drawing dominant dataset from big sensory data in wireless sensor networks, The 34th Annual IEEE International Conference on Computer Communications (INFOCOM 2015), (2015), 531-539.
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An adaptive approach to real-time aggregate monitoring with differential privacy, IEEE Transactions on Knowledge and Data Engineering, 26 (2014), 2094-2106.
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Latent-data Privacy Preserving with Customized Data Utility for Social Network Data, IEEE Transactions on Vehicular Technology, 67 (2018), 665-673.
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Approximate aggregation for tracking quantiles and range countings in wireless sensor networks, Theoretical Computer Science, 607 (2015), 381-390.
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[24] |
T. H. Hubert Chan, M. Li, E. Shi and W. Xu,
Differentially private continual monitoring of heavy hitters from distributed streams, Springer Berlin Heidelberg, (2012), 140-159.
|
[25] |
R. Kalman,
A new approach to linear filtering and prediction problem, Journal of Basic Engineering, 82 (1960), 35-45.
doi: 10.1115/1.3662552. |
[26] |
G. Kellaris, S. Papadopoulos, X. Xiao and D. Papadias,
Differentially private event sequences over infinite streams, Proc. of the VLDB Endowment, 7 (2014), 1155-1166.
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Towards an axiomatization of statistical privacy and utility, Proc. of ACM PODS, (2010), 147-158.
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[28] |
C. Li, M. Hay, V. Rastogi, G. Miklau and A. Mcgregor,
Optimizing linear counting queries under differential privacy, Twenty-Ninth ACM Sigmod-Sigact-Sigart Symposium on Principles of Database Systems, (2010), 123-134.
|
[29] |
J. Li, S. Cheng, Z. Cai, J. Yu, C. Wang and Y. Li, Approximate holistic aggregation in wireless sensor networks, 2015 IEEE 35th International Conference on Distributed Computing Systems, (2015).
doi: 10.1109/ICDCS.2015.86. |
[30] |
Y. Liang and Z. Cai and Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, (2017), Article ID 7576307, 12 pages.
doi: 10.1155/2017/7576307. |
[31] |
J. Mao, W. Tian, Y. Zhang, J. Cui, H. Ma, J. Bian, J. Liu and J. Zhang, Co-Check: Collab-orative outsourced data auditing in multicloud environment, Security and Communication Networks, (2017), Article ID 2948025, 13 pages
doi: 10.1155/2017/2948025. |
[32] |
J. Mao, Y. Zhang, P. Li, T. Li, Q. Wu and J. Liu,
A Position-aware Merkle Tree for Dynamic Cloud Data Integrity Verification, Soft Computing, 21 (2017), 2151-2164.
doi: 10.1007/s00500-015-1918-8. |
[33] |
H. Michael, R. Vibhor, M. Gerome and D. Suciu,
Boosting the accuracy of differentially private histograms through consistency, Proceedings of the Vldb Endowment, 3 (2010), 1021-1032.
|
[34] |
J. Sun, Y. Fang and X. Zhu,
Privacy and emergency response in e-healthcare leveraging wireless body sensor networks, IEEE Wireless Communications, 17 (2010), 66-73.
doi: 10.1109/MWC.2010.5416352. |
[35] |
B. Thomas,
A framework for generating network-based moving objects, Geoinformatica, 6 (2002), 153-180.
|
[36] |
Q. Wang, Y. Zhang, X. Lu and Z. Wang, RescueDP: Real-time spatio-temporal crowdsourced data publishing with differential privacy, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, (2016).
doi: 10.1109/INFOCOM.2016.7524458. |
[37] |
X. Xiao, G. Bender, H. Michael and G. Johannes,
iReduct: differential privacy with reduced relative errors, ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June, (2011), 229-240.
|
[38] |
J. Xu, Z. Zhang, X. Xiao, Y. Yang and G. Yu,
Differentially private histogram publication, IEEE International Conference on Data Engineering, (2012), 32-43.
|
[39] |
L. Zhang, Z. Cai and X. Wang,
FakeMask: A Novel Privacy Preserving Approach for Smartphones, IEEE Transactions on Network and Service Management, 13 (2016), 335-348.
doi: 10.1109/TNSM.2016.2559448. |
[40] |
X. Zheng, Z. Cai, J. Yu, C. Wang and Y. Li,
Follow but no track: Privacy preserved profile publishing in cyber-physical social systems, IEEE Internet of Things Journal, 4 (2017), 1868-1878.
doi: 10.1109/JIOT.2017.2679483. |
[41] |
CTR Data, Available from: https://www.kaggle.com/c/avazu-ctr-prediction. |
[42] |
Capital Bikeshare Data, Available from: https://www.capitalbikeshare.com/system-data. |
show all references
References:
[1] |
Administration, United States Federal Highway, Traffic monitoring guide, Evaluation, (2001). |
[2] |
K. Aleksandra, K. Krishnaram, M. Nina and N. Alexandros,
Releasing search queries and clicks privately, International Conference on World Wide Web, (2009), 171-180.
|
[3] |
J. Ankur,
Adaptive stream resource management using Kalman Filters, ACM SIGMOD International Conference on Management of Data, (2004), 11-22.
|
[4] |
A. Blum, K. Ligett and A. Roth, A learning theory approach to non-interactive database privacy, Journal of the ACM, 60 (2008), Art. 12, 25 pp.
doi: 10.1145/2450142.2450148. |
[5] |
C. A. Bradley, D. Rolka and J. Loonsk,
BioSense: Implementation of a national
early event detection and situational awareness system, Mmwr Supplements, 54 (2005), 11pp.
|
[6] |
Z. Cai, Z. He, X. Guan and Y. Li,
Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, PP (2016), 1-1.
doi: 10.1109/TDSC.2016.2613521. |
[7] |
J. Cao, Q. Xiao, G. Ghinita, N. Li, E. Bertino and T. Kian Lee,
Efficient and accurate strategies for differentially-private sliding window queries, International Conference on Extending Database Technology, (2013), 191-202.
|
[8] |
R. Chen, N. Mohammed, B. C. M. Fung, B. C. Desai and X. Li,
Publishing setvalued data via differential privacy, Vldb, 4 (2012), 1087-1098.
|
[9] |
S. Cheng, Z. Cai, J. Li and H. Gao,
Extracting kernel dataset from big sensory data in wireless sensor networks, IEEE Transactions on Knowledge and Data Engineering, 29 (2017), 813-827.
doi: 10.1109/TKDE.2016.2645212. |
[10] |
S. Cheng, Z. Cai and J. Li,
Curve query processing in wireless sensor networks, IEEE Transactions on Vehicular Technology, 64 (2015), 5198-5209.
doi: 10.1109/TVT.2014.2375330. |
[11] |
S. Cheng, Z. Cai, J. Li and X. Fang,
Drawing dominant dataset from big sensory data in wireless sensor networks, The 34th Annual IEEE International Conference on Computer Communications (INFOCOM 2015), (2015), 531-539.
doi: 10.1109/INFOCOM.2015.7218420. |
[12] |
D. Cynthia,
Differential privacy, International Colloquium on Automata, Languages, and Programming, 4052 (2006), 1-12.
doi: 10.1007/11787006_1. |
[13] |
D. Cynthia,
Differential privacy in new settings, Acm-Siam Symposium on Discrete Algorithms, (2010), 174-183.
|
[14] |
C. Dwork, M. Naor, T. Pitassiand and G. N. Rothblum,
Differential privacy under continual observation, STOC '10 Proceedings of the Forty-Second ACM Symposium on Theory of Computing, (2010), 715-724.
doi: 10.1145/1806689.1806787. |
[15] |
C. Dwork, M. Naor, O. Reingold, G. N. Rothblum and S. Vadhan,
On the complexity of differentially private data release: Efficient algorithms and hardness results, Proc. 41st Annu. ACM STOC, (2009), 381-390.
|
[16] |
C. Dwork, F. McSherry, K. Nissim and A. Smith,
Calibrating noise to sensitivity in private data analysis, Theory of Cryptography, 3876 (2006), 265-284.
doi: 10.1007/11681878_14. |
[17] |
L. Fan and L. Xiong,
An adaptive approach to real-time aggregate monitoring with differential privacy, IEEE Transactions on Knowledge and Data Engineering, 26 (2014), 2094-2106.
|
[18] |
T. Florian,
Privacy issues in vehicular ad hoc networks, International Conference on Privacy Enhancing Technologies, (2005), 197-209.
|
[19] |
B. J. Frey and D. Dueck,
Clustering by passing messages between data points, Science, 315 (2007), 972-976.
doi: 10.1126/science.1136800. |
[20] |
C. Graham, P. Cecilia, S. Divesh and T. T. L. Tran,
Differentially private summaries for sparse data, International Conference on Database Theory, (2011), 299-311.
|
[21] |
Z. He, Z. Cai and J. Yu,
Latent-data Privacy Preserving with Customized Data Utility for Social Network Data, IEEE Transactions on Vehicular Technology, 67 (2018), 665-673.
doi: 10.1109/TVT.2017.2738018. |
[22] |
Z. He, Z. Cai, S. Cheng and X. Wang,
Approximate aggregation for tracking quantiles and range countings in wireless sensor networks, Theoretical Computer Science, 607 (2015), 381-390.
doi: 10.1016/j.tcs.2015.07.056. |
[23] |
Th. H. Chan, E. Shi and D. Song, Private and continual release of statistics, Automata, Languages and Programming, Part Ⅱ, 405-417, Lecture Notes in Comput. Sci., 6199, Springer, Berlin, 2010.
doi: 10.1007/978-3-642-14162-1_34. |
[24] |
T. H. Hubert Chan, M. Li, E. Shi and W. Xu,
Differentially private continual monitoring of heavy hitters from distributed streams, Springer Berlin Heidelberg, (2012), 140-159.
|
[25] |
R. Kalman,
A new approach to linear filtering and prediction problem, Journal of Basic Engineering, 82 (1960), 35-45.
doi: 10.1115/1.3662552. |
[26] |
G. Kellaris, S. Papadopoulos, X. Xiao and D. Papadias,
Differentially private event sequences over infinite streams, Proc. of the VLDB Endowment, 7 (2014), 1155-1166.
doi: 10.14778/2732977.2732989. |
[27] |
D. Kifer and B.-R. Lin,
Towards an axiomatization of statistical privacy and utility, Proc. of ACM PODS, (2010), 147-158.
doi: 10.1145/1807085.1807106. |
[28] |
C. Li, M. Hay, V. Rastogi, G. Miklau and A. Mcgregor,
Optimizing linear counting queries under differential privacy, Twenty-Ninth ACM Sigmod-Sigact-Sigart Symposium on Principles of Database Systems, (2010), 123-134.
|
[29] |
J. Li, S. Cheng, Z. Cai, J. Yu, C. Wang and Y. Li, Approximate holistic aggregation in wireless sensor networks, 2015 IEEE 35th International Conference on Distributed Computing Systems, (2015).
doi: 10.1109/ICDCS.2015.86. |
[30] |
Y. Liang and Z. Cai and Q. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, (2017), Article ID 7576307, 12 pages.
doi: 10.1155/2017/7576307. |
[31] |
J. Mao, W. Tian, Y. Zhang, J. Cui, H. Ma, J. Bian, J. Liu and J. Zhang, Co-Check: Collab-orative outsourced data auditing in multicloud environment, Security and Communication Networks, (2017), Article ID 2948025, 13 pages
doi: 10.1155/2017/2948025. |
[32] |
J. Mao, Y. Zhang, P. Li, T. Li, Q. Wu and J. Liu,
A Position-aware Merkle Tree for Dynamic Cloud Data Integrity Verification, Soft Computing, 21 (2017), 2151-2164.
doi: 10.1007/s00500-015-1918-8. |
[33] |
H. Michael, R. Vibhor, M. Gerome and D. Suciu,
Boosting the accuracy of differentially private histograms through consistency, Proceedings of the Vldb Endowment, 3 (2010), 1021-1032.
|
[34] |
J. Sun, Y. Fang and X. Zhu,
Privacy and emergency response in e-healthcare leveraging wireless body sensor networks, IEEE Wireless Communications, 17 (2010), 66-73.
doi: 10.1109/MWC.2010.5416352. |
[35] |
B. Thomas,
A framework for generating network-based moving objects, Geoinformatica, 6 (2002), 153-180.
|
[36] |
Q. Wang, Y. Zhang, X. Lu and Z. Wang, RescueDP: Real-time spatio-temporal crowdsourced data publishing with differential privacy, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, (2016).
doi: 10.1109/INFOCOM.2016.7524458. |
[37] |
X. Xiao, G. Bender, H. Michael and G. Johannes,
iReduct: differential privacy with reduced relative errors, ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June, (2011), 229-240.
|
[38] |
J. Xu, Z. Zhang, X. Xiao, Y. Yang and G. Yu,
Differentially private histogram publication, IEEE International Conference on Data Engineering, (2012), 32-43.
|
[39] |
L. Zhang, Z. Cai and X. Wang,
FakeMask: A Novel Privacy Preserving Approach for Smartphones, IEEE Transactions on Network and Service Management, 13 (2016), 335-348.
doi: 10.1109/TNSM.2016.2559448. |
[40] |
X. Zheng, Z. Cai, J. Yu, C. Wang and Y. Li,
Follow but no track: Privacy preserved profile publishing in cyber-physical social systems, IEEE Internet of Things Journal, 4 (2017), 1868-1878.
doi: 10.1109/JIOT.2017.2679483. |
[41] |
CTR Data, Available from: https://www.kaggle.com/c/avazu-ctr-prediction. |
[42] |
Capital Bikeshare Data, Available from: https://www.capitalbikeshare.com/system-data. |





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