Privacy-Preserving Collaborative Medical Time Series Analysis Based on Dynamic Time Warping

Liu, Xiaoning; Yi, Xun

doi:10.1007/978-3-030-29962-0_21

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…Since medical data security has become a growing public concern, a considerable number of schemes have been published for secure medical data sharing and privacy preservation [4,[6][7][8][9][10][11][12][13][14][15][16][17]. For instance, most research in protecting medical data have emphasised the use of cryptographic methods such as CP-ABE and KP-ABE [9,10,13,14,16,18].…”

Section: Related Workmentioning

confidence: 99%

E-Tenon: An efficient privacy-preserving secure open data sharing scheme for EHR system

Gope,

Lin,

Yang

et al. 2024

JCS

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The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is time-consuming. On the other hand, the use of EHR for medical research (e.g., development of precision medicine and diagnostics techniques) and optimisation of practices in healthcare organisations require the EHR to be analysed. To achieve that, they should be easily accessible without compromising the patient’s privacy. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desired security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under their own multi-level, fine-grained access policies. Analyses show that our system outperforms existing solutions in terms of computational complexity.

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Section: Related Workmentioning

confidence: 99%

E-Tenon: An efficient privacy-preserving secure open data sharing scheme for EHR system

Gope,

Lin,

Yang

et al. 2024

JCS

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“…Previous works aiming to protect the privacy of time-series data employed secure aggregation techniques to enable the computation of simple statistics and analytics [70], [84], [104], whereas others combined secure aggregation with differential privacy to bound the leakage stemming from the computations [113], [19]. More recently, Liu et al [76] employed secure multi-party computation (SMC) techniques for privacypreserving collaborative medical time-series analysis, based on dynamic time warping. Dauterman et al [31] use function secret sharing (FSS) to support various functionalities, e.g., multi-predicate filtering, on private time-series databases.…”

Section: A Privacy-preserving Time-seriesmentioning

confidence: 99%

Privacy-Preserving Federated Recurrent Neural Networks

Sav¹,

Abdulrahman²,

Pyrgelis³

et al. 2022

Preprint

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We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a federated learning setting by relying on multiparty homomorphic encryption (MHE). RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates the federated learning attacks that target the gradients under a passive-adversary threat model. We propose a novel packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, we also provide several clip-by-value approximations for enabling gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distribution among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.

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