Deep learning (DL) is a promising area of machine learning which is becoming popular due to its remarkable accuracy when trained with a massive amount of data. Often, these datasets are highly sensitive crowd-sourced data such as medical data, financial data, or image data, and the DL models trained on these data tend to leak privacy. We propose a new local differentially private (LDP) algorithm (named LATENT) which redesigns the training process in a way that a data owner can add a randomization layer before data leave data owners' devices and reach to a potentially untrusted machine learning service. This way LATENT prevents privacy leaks of DL models, e.g., due to membership inference and memorizing model attacks, while providing excellent accuracy. By not requiring a trusted party, LATENT can be more practical for cloud-based machine learning services in comparison to existing differentially private approaches. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91 - 96 ) with high model quality even under very low privacy budgets (e.g. @math ), outperforming existing differentially private approaches for deep learning.
Data perturbation may allow some privacy leak since the data results are released in their original format @cite_23 . Hence, a privacy model should identify the limits of private information protection disclosure mechanism @cite_48 . Earlier privacy models include @math @cite_47 , @math @cite_12 , @math @cite_46 , @math @cite_26 . It has been shown that these models are vulnerable to different attacks such as minimality attack @cite_17 , composition based attacks @cite_4 and foreground knowledge @cite_15 attacks. Differential privacy (DP) is trusted to provide a better level of privacy guarantee compared to previous privacy models @cite_27 @cite_19 @cite_5 .
@cite_47 @cite_26 @cite_4 @cite_15 @cite_48 @cite_19 @cite_27 @cite_23 @cite_5 @cite_46 @cite_12 @cite_17
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Private regression has received attention from both database and security communities. Recent work by (USENIX Security 2014) analyzed the functional mechanism ( VLDB 2012) for training linear regression models over medical data. Unfortunately, they found that model accuracy is already unacceptable with differential privacy when @math . We address this issue, presenting an explicit connection between differential privacy and stable learning theory through which a substantially better privacy utility tradeoff can be obtained. Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks. Since output perturbation is simple to implement, it means that our approach is potentially widely applicable in practice. We go on to apply it on the same medical data as used by Encouragingly, we achieve accurate models even for @math . In the last part of this paper, we study the impact of our improved differentially private mechanisms on model inversion attacks, a privacy attack introduced by We observe that the improved tradeoff makes the resulting differentially private model more susceptible to inversion attacks. We analyze this phenomenon formally. As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a framework to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy-preserving protocol for composing a differentially private aggregate classifier using classifiers trained locally by separate mutually untrusting parties. The protocol allows these parties to interact with an untrusted curator to construct additive shares of a perturbed aggregate classifier. We also present a detailed theoretical analysis containing a proof of differential privacy of the perturbed aggregate classifier and a bound on the excess risk introduced by the perturbation. We verify the bound with an experimental evaluation on a real dataset. We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d 1 ,..,d n , with a query being a subset q ⊆ [n] to be answered by Σ ieq d i . Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude (Ω√n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude O(√n).For time-T bounded adversaries we demonstrate a privacypreserving access algorithm whose perturbation magnitude is ≈ √T. With the increasing prevalence of information networks, research on privacy-preserving network data publishing has received substantial attention recently. There are two streams of relevant research, targeting different privacy requirements. A large body of existing works focus on preventing node re-identification against adversaries with structural background knowledge, while some other studies aim to thwart edge disclosure. In general, the line of research on preventing edge disclosure is less fruitful, largely due to lack of a formal privacy model. The recent emergence of differential privacy has shown great promise for rigorous prevention of edge disclosure. Yet recent research indicates that differential privacy is vulnerable to data correlation, which hinders its application to network data that may be inherently correlated. In this paper, we show that differential privacy could be tuned to provide provable privacy guarantees even in the correlated setting by introducing an extra parameter, which measures the extent of correlation. We subsequently provide a holistic solution for non-interactive network data publication. First, we generate a private vertex labeling for a given network dataset to make the corresponding adjacency matrix form dense clusters. Next, we adaptively identify dense regions of the adjacency matrix by a data-dependent partitioning process. Finally, we reconstruct a noisy adjacency matrix by a novel use of the exponential mechanism. To our best knowledge, this is the first work providing a practical solution for publishing real-life network data via differential privacy. Extensive experiments demonstrate that our approach performs well on different types of real-life network datasets.