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SCIENCE CHINA Information Sciences, Volume 63 , Issue 12 : 220303(2020) https://doi.org/10.1007/s11432-019-2834-x

Generative adversarial networks enhanced location privacy in 5G networks

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  • ReceivedOct 21, 2019
  • AcceptedMar 12, 2020
  • PublishedNov 4, 2020

Abstract


Acknowledgment

This work was partly supported by JSPS KAKENHI (Grant No. JP19H04105).


References

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  • Figure 1

    (Color online) Comparison between raw dataset (a) and generated synthetic dataset (b). The two figures show the correlations and distributions of each attribute before and after processing. The overall statistic features are quite similar, which guarantees high-level data utility.

  • Figure 2

    (Color online) Data utility evaluation. In the two figures, we compare the data utility in two cases where the iteration times are $1000$ and $10000$, respectively. The improvement of accuracy is significant with the increase of the iteration times. (a) $\epsilon~=~3$, it = 1000; (b) $\epsilon~=~1$, it = 10000.

  • Figure 3

    (Color online) Privacy protection evaluation. This figure reveals the privacy protection level upgrades with the decrease of $\epsilon$, which is the index of privacy protection level under differential privacy framework. (a) $\epsilon~=~3$, it = 1000;protect łinebreak (b) $\epsilon~=~1$, it = 10000.

  • Figure 4

    (Color online) Convergence v.s. learning rate. The learning rates are 0.2, 0.3, and 0.6, respectively. In all cases, the proposed model can achieve an convergence while greater learning rates accelerates the convergence.

  • Table 1  

    Table 1Preliminary of the real-wolrd data set

    Property $s$-longitude1 $s$-latitude1 $s$-longitude2 $s$-latitude2
    Count 150.00 150.00 150.00 150.00
    Mean 5.84 3.05 3.76 1.20
    Std 0.83 0.43 1.76 0.76
    Min 4.30 2.00 1.00 0.10
    25% 5.10 2.80 1.60 0.30
    50% 5.80 3.00 4.35 1.30
    75% 6.40 3.30 5.10 1.80
    Max 7.90 4.40 6.90 2.50
  •   

    Algorithm 1 Differentially private posterior sample estimator

    Require:Dataset $D$, privacy budget $\epsilon$, log-likelihood function $f(\cdot|\cdot)$ satisfying ${\rm~sup}_{x,\theta}~\|f(x|\theta\|~\leq~B)$, a prior $\pi(\cdot)$.

    Output:$\theta'~\sim~P(\theta|X)~\propto~\mathrm(\rm~exp)(\sum^N_{i=1}~f'(\theta|x_i))~\pi'(\theta)$.

    Set $\rho~=~{\rm~min}{1,~\frac{\epsilon}{4B}}$;

    Formulate log-likelihood function and the prior $f'(\cdot|\cdot)~=~\rho~f(\cdot|\cdot)$ and $\pi'~=~(\pi(\cdot))^{\rho}$;

    Return $\theta'$.

  •   

    Algorithm 2 Differentially private data augmentation

    Require:Sampled dataset $D_r$, size of the original dataset.

    Output:Full-sized differentially private synthetic dataset.

    Establish encoder EN and segmentation loss $\mathfrak{L}_{\rm~seg}$;

    Establish hybrid weight sharing loss $\mathfrak{L}_{w_{D_r}}$ and $\mathfrak{L}_{w_{D_n}}$;

    Establish circle consistence loss $\mathfrak{L}_{\rm~cc}$;

    Establish adversarial learning loss $\mathfrak{L}_{{\rm~GAN}_1}$ and $\mathfrak{L}_{{\rm~GAN}_2}$;

    Integrate all loss to the final objective function $\mathfrak{L}$;

    Return the full-sized differentially private synthetic dataset.