Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing

Split inference partitions a deep neural network (DNN) to run the early part at the edge and the later part in the cloud. This meets two key requirements for on-device machine learning: input privacy and compute efficiency. Still, an open question in split inference is output privacy, given that the output of a DNN is visible to the cloud. While encrypted computing can protect output privacy, it mandates extensive computation and communication resources. In this paper, we introduce "Salted DNNs": a novel method that lets clients control the semantic interpretation of DNN output at inference time while maintaining accuracy and efficiency very close to that of a standard DNN. Experimental evaluations conducted on both image and sensor data show that Salted DNNs achieve classification accuracy very close to standard DNNs, particularly when the salted layer is positioned within the early part to meet the requirements of split inference. Our method is general and can be applied to various DNNs. We open-source our code and results, as a benchmark for future studies.

Latent Masking for Multimodal Self-supervised Learning in Health Timeseries

Limited availability of labeled data for machine learning on biomedical time-series hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learning data representations without labels. However, current SSL methods require expensive computations for negative pairs and are designed for single modalities, limiting their versatility. To overcome these limitations, we introduce CroSSL (Cross-modal SSL). CroSSL introduces two novel concepts: masking intermediate embeddings from modality-specific encoders and aggregating them into a global embedding using a cross-modal aggregator. This enables the handling of missing modalities and end-to-end learning of cross-modal patterns without prior data preprocessing or time-consuming negative-pair sampling. We evaluate CroSSL on various multimodal time-series benchmarks, including both medical-grade and consumer biosignals. Our results demonstrate superior performance compared to previous SSL techniques and supervised benchmarks with minimal labeled data. We additionally analyze the impact of different masking ratios and strategies and assess the robustness of the learned representations to missing modalities. Overall, our work achieves state-of-the-art performance while highlighting the benefits of masking latent embeddings for cross-modal learning in temporal health data.

Vicious Classifiers: Data Reconstruction Attack at Inference Time

Privacy-preserving inference via edge or encrypted computing paradigms encourages users of machine learning services to confidentially run a model on their personal data for a target task and only share the model's outputs with the service provider; e.g., to activate further services. Nevertheless, despite all confidentiality efforts, we show that a ''vicious'' service provider can approximately reconstruct its users' personal data by observing only the model's outputs, while keeping the target utility of the model very close to that of a ''honest'' service provider. We show the possibility of jointly training a target model (to be run at users' side) and an attack model for data reconstruction (to be secretly used at server's side). We introduce the ''reconstruction risk'': a new measure for assessing the quality of reconstructed data that better captures the privacy risk of such attacks. Experimental results on 6 benchmark datasets show that for low-complexity data types, or for tasks with larger number of classes, a user's personal data can be approximately reconstructed from the outputs of a single target inference task. We propose a potential defense mechanism that helps to distinguish vicious vs. honest classifiers at inference time. We conclude this paper by discussing current challenges and open directions for future studies. We open-source our code and results, as a benchmark for future work.

Centaur: Federated Learning for Constrained Edge Devices

Federated learning (FL) on deep neural networks facilitates new applications at the edge, especially for wearable and Internet-of-Thing devices. Such devices capture a large and diverse amount of data, but they have memory, compute, power, and connectivity constraints which hinder their participation in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained devices to efficiently participate in FL on large neural nets. Centaur combines two major ideas: (i) a data selection scheme to choose a portion of samples that accelerates the learning, and (ii) a partition-based training algorithm that integrates both constrained and powerful devices owned by the same user. Evaluations, on four benchmark neural nets and three datasets, show that Centaur gains ~10% higher accuracy than local training on constrained devices with ~58% energy saving on average. Our experimental results also demonstrate the superior efficiency of Centaur when dealing with imbalanced data, client participation heterogeneity, and various network connection probabilities.

Efficient Hyperparameter Optimization for Differentially Private Deep Learning

Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge. Unlike the typical SGD, private datasets cannot be used many times for hyperparameter search in DPSGD; e.g., via a grid search. Therefore, there is an essential need for algorithms that, within a given search space, can find near-optimal hyperparameters for the best achievable privacy-utility tradeoffs efficiently. We formulate this problem into a general optimization framework for establishing a desirable privacy-utility tradeoff, and systematically study three cost-effective algorithms for being used in the proposed framework: evolutionary, Bayesian, and reinforcement learning. Our experiments, for hyperparameter tuning in DPSGD conducted on MNIST and CIFAR-10 datasets, show that these three algorithms significantly outperform the widely used grid search baseline. As this paper offers a first-of-a-kind framework for hyperparameter tuning in DPSGD, we discuss existing challenges and open directions for future studies. As we believe our work has implications to be utilized in the pipeline of private deep learning, we open-source our code at https://github.com/AmanPriyanshu/DP-HyperparamTuning.

Quantifying Information Leakage from Gradients

Sharing deep neural networks' gradients instead of training data could facilitate data privacy in collaborative learning. In practice however, gradients can disclose both private latent attributes and original data. Mathematical metrics are needed to quantify both original and latent information leakages from gradients computed over the training data. In this work, we first use an adaptation of the empirical $\mathcal{V}$-information to present an information-theoretic justification for the attack success rates in a layer-wise manner. We then move towards a deeper understanding of gradient leakages and propose more general and efficient metrics, using sensitivity and subspace distance to quantify the gradient changes w.r.t. original and latent information, respectively. Our empirical results, on six datasets and four models, reveal that gradients of the first layers contain the highest amount of original information, while the classifier/fully-connected layers placed after the feature extractor contain the highest latent information. Further, we show how training hyperparameters such as gradient aggregation can decrease information leakages. Our characterization provides a new understanding on gradient-based information leakages using the gradients' sensitivity w.r.t. changes in private information, and portends possible defenses such as layer-based protection or strong aggregation.

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs

It is known that deep neural networks, trained for the classification of a non-sensitive target attribute, can reveal sensitive attributes of their input data; through features of different granularity extracted by the classifier. We, taking a step forward, show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data, at inference time, into the classifier's outputs for the target attribute. An attack that works even if users have a white-box view of the classifier, and can keep all internal representations hidden except for the classifier's estimation of the target attribute. We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute. Our evaluations on several tasks in real-world datasets show that a semi-trusted server can build a classifier that is not only perfectly honest but also accurately curious. Our work highlights a vulnerability that can be exploited by malicious machine learning service providers to attack their user's privacy in several seemingly safe scenarios; such as encrypted inferences, computations at the edge, or private knowledge distillation. We conclude by showing the difficulties in distinguishing between standard and HBC classifiers and discussing potential proactive defenses against this vulnerability of deep classifiers.

Dopamine: Differentially Private Federated Learning on Medical Data

While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed datasets, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure aggregation, can establish a better trade-off between differential privacy (DP) guarantee and DNN's accuracy than other approaches. Results on a diabetic retinopathy~(DR) task show that Dopamine provides a DP guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel DP where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health.

Dopamine: Differentially Private Secure Federated Learning on Medical Data

While rich medical datasets are hosted in hospitals distributed across countries, concerns on patients' privacy is a barrier against utilizing such data to train deep neural networks (DNNs) for medical diagnostics. We propose Dopamine, a system to train DNNs on distributed medical data, which employs federated learning (FL) with differentially-private stochastic gradient descent (DPSGD), and, in combination with secure multi-party aggregation, can establish a better privacy-utility trade-off than the existing approaches. Results on a diabetic retinopathy (DR) task show that Dopamine provides a privacy guarantee close to the centralized training counterpart, while achieving a better classification accuracy than FL with parallel differential privacy where DPSGD is applied without coordination. Code is available at https://github.com/ipc-lab/private-ml-for-health.