Artificial intelligence (AI) has been successfully applied in numerous scientific domains including biomedicine and healthcare. Here, it has led to several breakthroughs ranging from clinical decision support systems, image analysis to whole genome sequencing. However, training an AI model on sensitive data raises also concerns about the privacy of individual participants. Adversary AIs, for example, can abuse even summary statistics of a study to determine the presence or absence of an individual in a given dataset. This has resulted in increasing restrictions to access biomedical data, which in turn is detrimental for collaborative research and impedes scientific progress. Hence there has been an explosive growth in efforts to harness the power of AI for learning from sensitive data while protecting patients' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy, and discusses their strengths, limitations, and open problems.

Privacy-preserving Artificial Intelligence Techniques in Biomedicine

Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying $(\epsilon, \delta)$-differential privacy (DP) roughly means that, except with a small probability $\delta$, altering a record in a dataset cannot change the probability that an output is seen by more than a multiplicative factor $e^{\epsilon} $. A well-known solution to $(\epsilon, \delta)$-DP is the Gaussian mechanism initiated by Dwork et al. [1] in 2006 with an improvement by Dwork and Roth [2] in 2014, where a Gaussian noise amount $\sqrt{2\ln \frac{2}{\delta}} \times \frac{\Delta}{\epsilon}$ of [1] or $\sqrt{2\ln \frac{1.25}{\delta}} \times \frac{\Delta}{\epsilon}$ of [2] is added independently to each dimension of the query result, for a query with $\ell_2$-sensitivity $\Delta$. Although both classical Gaussian mechanisms [1,2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1,2] do not achieve $(\epsilon,\delta)$-DP. We obtain such result by analyzing the optimal noise amount $\sigma_{DP-OPT}$ for $(\epsilon,\delta)$-DP and identifying $\epsilon$ and $\delta$ where the noise amounts of classical mechanisms are even less than $\sigma_{DP-OPT}$. 
Since $\sigma_{DP-OPT}$ has no closed-form expression and needs to be approximated in an iterative manner, we propose Gaussian mechanisms by deriving closed-form upper bounds for $\sigma_{DP-OPT}$. Our mechanisms achieve $(\epsilon,\delta)$-DP for any $\epsilon$, while the classical mechanisms [1,2] do not achieve $(\epsilon,\delta)$-DP for large $\epsilon$ given $\delta$. Moreover, the utilities of our mechanisms improve those of [1,2] and are close to that of the optimal yet more computationally expensive Gaussian mechanism.

Reviewing and Improving the Gaussian Mechanism for Differential Privacy.

DP-GAN: Differentially private consecutive data publishing using generative adversarial nets

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. 
In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy experts

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Privacy is one of the most important properties of an information system must satisfy. In which systems the need to share information among different, not trusted entities, the protection of sensible information has a relevant role. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when data mining techniques are used in a malicious way. Privacy preserving data mining algorithms have been recently introduced with the aim of preventing the discovery of sensible information. In this paper we propose a modification to privacy preserving association rule mining on distributed homogenous database algorithm. Our algorithm is faster than old one which modified with preserving privacy and accurate results. Modified algorithm is based on a semi-honest model with negligible collision probability. The flexibility to extend to any number of sites without any change in implementation can be achieved. And also any increase doesn't add more time to algorithm because all client sites perform the mining in the same time so the overhead in communication time only. The total bit-communication cost for our algorithm is function in (N) sites.

Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Data Base.

With the development of smart city, more organizations analyze people's trajectory data, so as to provide better location-based services. However, publishing the original trajectory data directly raises serious privacy threats to individuals. As a kind of powerful framework for providing formal and strong privacy guarantees, differential privacy has been applied in the trajectory data publication. Nevertheless, the existing approaches assume that individuals require the same privacy preference, and thus the same level of privacy protection is provided for all individuals, which leads to insufficient privacy guarantee is provided for some individuals, while the other individuals received excess privacy protection. This paper assumes that individuals require different level of privacy and propose a personalized differential privacy publication mechanism for trajectory data. We apply the Hilbert curve to extract the distribution characteristics of the trajectory data at each time and propose a personalized different privacy generalization algorithm for trajectories with different privacy preferences. Through extensive experiments on real world trajectory dataset, we show that this mechanism provides better tradeoff between data privacy and utility compared with the uniform differential privacy based methods.

A Novel Personalized Differential Privacy Mechanism for Trajectory Data Publication

Attribute-based searchable encryption (ABSE) is a promising encryption mechanism for sharing outsourced encrypted data in clouds, allowing fine-grained access control over data while searching for encrypted data. However, the access policy in the most existing ABSE schemes exists in plaintext, which could expose sensitive information about legitimate data users. Moreover, such schemes delegate complex search operations to a cloud server, which can lead to data tampering and even untrusted results, and single point of failure. In this paper, we propose a blockchain-based anonymous attribute-based searchable encryption scheme for data sharing (BADS). First, attributes of the access policy are hidden, thus providing confidentiality to the set of attributes that satisfy the access policy. Then combining ABSE with blockchain have features of tamper-proof, integrity verification and non-repudiation. In particular, information such as secure index is stored in blockchain, while encrypted data is stored in a distributed system called the InterPlanetary File System (IPFS) to avoid single point of failure. Finally, BADS supports the matching algorithm that perform a fixed number of pairing operations before searching algorithm. We analysis security and evaluate performance to show the efficiency and practicability of BADS. 

A Blockchain-based Anonymous Attribute-based Searchable Encryption Scheme for Data Sharing

Differential privacy has broadly applied to statistical analysis, and its mainly objective is to ensure the tradeoff between the utility of noise data and the privacy preserving of individual’s sensitive information However, an individual could not achieve expected data utility under differential privacy mechanisms, since the adding noise is random To this end, we proposed an adaptive Gaussian mechanism based on expected data utility under conditional filtering noise Firstly, this paper made conditional filtering for Gaussian mechanism noise Secondly, we defined the expected data utility according to the absolute value of relative error Finally, we presented an adaptive Gaussian mechanism by combining expected data utility with conditional filtering noise Through comparative analysis, the adaptive Gaussian mechanism satisfies differential privacy and achieves expected data utility for giving any privacy budget Furthermore, our scheme is easy extend to engineering implementation

Adaptive Gaussian Mechanism Based on Expected Data Utility under Conditional Filtering Noise

The development of deep learning technology has promoted the application of artificial intelligence in the field of education, and teaching gestures are widely used by teachers in the teaching process. In order to further analyze how teachers' gestures can improve teaching effects and quality, and to make up for the lack of teaching gestures image recognition function and action anthropomorphism, which are currently lacking in educational robots, it is necessary to analyze the different gestures used by teachers in teaching. The key point is how scientific and efficient to recognize all kinds of gestures used by the teachers. Based on the unique convolutional layer and pooling layer structure of convolutional neural network, this paper analyzed five types of online teaching gestures images marked by the human key points detection, including indicative gestures, one-hand beat gestures, two-hand beat gestures, frontal habitual gestures, and lateral habitual gestures. An online teaching gestures recognition model with a ten-layer convolutional neural network is established. The error of this model is minimized by error back propagation and gradient descent algorithms, and the corresponding model parameters are obtained. The model is verified by the test set, and the training accuracy of the model is as high as 96%. The model can be applied to analyze the influence of the teachers' teaching gestures on the teaching effect and quality, at the same time, it can improve the intelligence and anthropomorphism of educational robots in the field of computer vision and its human-computer interaction.

Online Teaching Gestures Recognition Model Based on Deep Learning

Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and utility metrics. However, there is no unified trade-off measurement of differential privacy mechanisms. To this end, we proposed the definition of privacy-preserving monotonicity of differential privacy, which measured the trade-off between privacy and utility. First, to formulate the trade-off, we presented the definition of privacy-preserving monotonicity based on computational indistinguishability. Second, building on privacy metrics of the expected estimation error and entropy, we theoretically and numerically showed privacy-preserving monotonicity of Laplace mechanism, Gaussian mechanism, exponential mechanism, and randomized response mechanism. In addition, we also theoretically and numerically analyzed the utility monotonicity of these several differential privacy mechanisms based on utility metrics of modulus of characteristic function and variant of normalized entropy. Third, according to the privacy-preserving monotonicity of differential privacy, we presented a method to seek trade-off under a semi-honest model and analyzed a unilateral trade-off under a rational model. Therefore, privacy-preserving monotonicity can be used as a criterion to evaluate the trade-off between privacy and utility in differential privacy mechanisms under the semi-honest model. However, privacy-preserving monotonicity results in a unilateral trade-off of the rational model, which can lead to severe consequences.

Privacy-Preserving Monotonicity of Differential Privacy Mechanisms

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