Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Differential privacy for publishing enterprise-scale WLAN traces

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Wireless trace data play an important role in wireless network researches. However, publishing the raw WLAN traces poses potential privacy risks of network users. Therefore, it is necessary to sanitize users’ sensitive information before these traces are published, and provide high data utility for wireless network researches as well. Although some existing works based on various anonymization methods have started to address the problem of sanitizing WLAN traces, we find the anonymization techniques cannot provide strong and provable privacy guarantee by analyzing a real WLAN trace dataset. Differential Privacy is the only framework that can provide strong and provable privacy guarantee. However, our analysis shows that existing studies on differential privacy fail to provide effective data utility for query operations on multi-dimensional and large-scale datasets. Aiming at WLAN trace datasets that have unique characteristics of multi-dimensional and large-scale, this paper proposes a privacy-preserving data publishing algorithm which not only satisfies differential privacy but also realizes high data utility for query operations. We prove that the proposed sanitization algorithm satisfies \(\epsilon \)-differential privacy. Furthermore, the theoretical analysis shows the noise variance in our sanitization algorithm is \(O(\log ^{o(1)}n/\epsilon ^2)\) which indicates the algorithm can achieve high data utility on large-scale datasets. Moreover, from the results of extensive experiments on an enterprise-scale WLAN trace dataset, we also show that our sanitization algorithm can provide high data utility for query operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bao H, Lu R (2015) A new differentially private data aggregation with fault tolerance for smart grid communications. IEEE Internet Things 2(3):248–258

    Article  Google Scholar 

  • Barak B, Chaudhuri K, Dwork C et al (2007) Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: Proceedings of the 26th ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, pp 273–282

  • Chakrabarti K, Garofalakis M, Rastogi R et al (2001) Approximate query processing using wavelets. Int J Very Large Data Bases 10(2–3):199–223

    MATH  Google Scholar 

  • Chen R, Mohammed N, Fung BC et al (2011) Publishing set- valued data via differential privacy. In: Proceedings of the VLDB endowment, pp 1087–1098

  • Chen R, Acs G, Castelluccia C (2012) Differentially private sequential data publication via variable-length n-grams. In: Proceedings of the 2012 ACM conference on computer and communications security, pp 638–649

  • Chow CY, Mokbel MF (2011) Trajectory privacy in location-based services and data publication. ACM SIGKDD Explor Newsl 13(1):19–29

    Article  Google Scholar 

  • Coull SE, Wright CV, Monrose F et al (2007) Playing devils advocate: Inferring sensitive information from anonymized network traces. In: Proceedings of the 14th network and distributed system security symposium. ISOC, pp 35–47

  • De Oliveira EC, De Albuquerque CV (2009) Nectar: a dtn routing protocol based on neighborhood contact history. In: Proceedings of the 2009 ACM symposium on applied computing, ACM, pp 40–46

  • Domingo-Ferrer J, Mateo-Sanz JM (2002) Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans Knowl Data Eng 14(1):189–201

    Article  Google Scholar 

  • Dwork C (2008) Differential privacy: a survey of results. In: Proceedings of theory and applications of models of computation, pp 1–19

  • Dwork C (2011) Differential privacy. Encyclopedia of Cryptography and Security, pp 338–340

  • Dwork C, McSherry F, Nissim K et al (2006) Calibrating noise to sensitivity in private data analysis. Theory of cryptography, pp 265-284

  • Ester M, Kohlhammer J, Kriegel HP (2000) The dc-tree: a fully dynamic index structure for data warehouses. In: Proceedings of the 16th IEEE international conference on data engineering, pp 379–388

  • Ganta SR, Kasiviswanathan SP, Smith A (2008) Composition attacks and auxiliary information in data privacy. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 265–273

  • Geng Q, Viswanath P (2016) Optimal noise adding mechanisms for approximate differential privacy. IEEE Trans Inf Theory 62(2):952–969

    Article  MathSciNet  MATH  Google Scholar 

  • Hay M, Rastogi V, Miklau G et al (2010) Boosting the accuracy of differentially private histograms through consistency. In: Proceedings of the VLDB endowment, pp 1021–1032

  • Kairouz P, Oh S, Viswanath P (2017) The composition theorem for differential privacy. IEEE Trans Inf Theory 63(6):4037–4049

    Article  MathSciNet  MATH  Google Scholar 

  • Kim JW, Kim DH, Jang B (2018) Application of local differential privacy to collection of indoor positioning data. IEEE Access 6:4276–4286

    Article  Google Scholar 

  • Kumar U, Helmy A (2009) Human behavior and challenges of anonymizing wlan traces. In: Proceedings of the 2009 IEEE global telecommunications conference, Honolulu, USA, pp 1–6

  • LeFevre K, DeWitt DJ, Ramakrishnan R (2006) Mondrian multidimensional k-anonymity. In: Proceedings of the 22nd international conference on data engineering, pp 1–12

  • Li N, Li T, Venkatasubramanian S (2007) t-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of the 23rd IEEE international conference on data engineering, pp 106–115

  • Machanavajjhala A, Kifer D, Gehrke J et al (2007) l-diversity: privacy beyond k-anonymity. ACM Trans Knowl Discov Data 1(1):3–55

    Article  Google Scholar 

  • McSherry F, Mahajan R (2011) Differentially-private network trace analysis. ACM SIGCOMM Comput Commun Rev 41(4):123–134

    Article  Google Scholar 

  • Mohammed N, Chen R, Fung B et al (2011) Differentially private data release for data mining. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 493–501

  • Roussopoulos N, Kotidis Y, Roussopoulos M (1997) Cubetree: organization of and bulk incremental updates on the data cube. In: Proceedings of ACM SIGMOD record, pp 89–99

  • Sarat S, Terzis A (2007) On the detection and origin identification of mobile worms. In: Proceedings of the 2007 ACM workshop on recurring Malcode, pp 54–60

  • Sei Y, Ohsuga A (2017) Differential private data collection and analysis based on randomized multiple dummies for untrusted mobile crowdsensing. IEEE Trans Inf Forensics Secur 12(4):926–939

    Article  Google Scholar 

  • Slagell AJ, Lakkaraju K, Luo K (2006) Flaim: a multi-level anonymization framework for computer and network logs. In: LISA, pp 3–8

  • Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 10(5):557–70

    Article  MathSciNet  MATH  Google Scholar 

  • Tan K, Yan G, Yeo J et al (April 2011) Privacy analysis of user association logs in a large-scale wireless lan. In: Proceedings of the 2011 international conference on computer communications, Shanghai, China, pp 31–35

  • To H, Ghinita G, Fan L et al (2017) Differentially private location protection for worker datasets in spatial crowdsourcing. IEEE Trans Mob Comput 16(4):934–949

    Google Scholar 

  • Tong W, Hua J, Zhong S (2017) A jointly differentially private scheduling protocol for ridesharing services. IEEE Trans Inf Forensics Secur 12(10):2444–2456

    Article  Google Scholar 

  • Wang Y, Huang Z, Mitra S et al (2017) Differential privacy in linear distributed control systems: entropy minimizing mechanisms and performance tradeoffs. IEEE Trans Control Netw Syst 4(1):118–130

    Article  MathSciNet  MATH  Google Scholar 

  • Wang Q, Chen D, Zhang N et al (2017) PCP: a privacy-preserving content-based publish-subscribe scheme with differential privacy in fog computing. IEEE Access 5:17962–17974

    Article  Google Scholar 

  • Wj Hsu, Dutta D, Helmy A (2012) Structural analysis of user association patterns in university campus wireless lans. IEEE Trans Mob Comput 11(11):1734–1748

    Article  Google Scholar 

  • Wong RCW, Fu AWC, Wang K et al (2009) Anonymization-based attacks in privacy-preserving data publishing. ACM Trans Database Syst 34(2):1–46

    Article  Google Scholar 

  • Xiao X, Bender G, Hay M et al (2011a) ireduct: differential privacy with reduced relative errors. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, pp 229–240

  • Xiao X, Wang G, Gehrke J (2011b) Differential privacy via wavelet transforms. IEEE Trans Knowl Data Eng 23(8):1200–1214

    Article  Google Scholar 

  • Xu J, Zhang Z, Xiao X et al (2013) Differentially private histogram publication. Int J Very Large Data Bases 22(6):797–822

    Article  Google Scholar 

  • Zhu T, Li G, Zhou W et al (2017) Differentially private data publishing and analysis: a survey. IEEE Trans Knowl Data Eng 28(8):1619–1638

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Dazhi Li, Xin Dong & Minglu Li

  2. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China

    Dazhi Li

Authors
  1. Dazhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minglu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dazhi Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, D., Dong, X. & Li, M. Differential privacy for publishing enterprise-scale WLAN traces. Soft Comput 23, 5667–5682 (2019). https://doi.org/10.1007/s00500-018-3223-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3223-9

Keywords

Search

Navigation