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1. LIFELONG DP: CONSISTENTLY BOUNDED DIFFERENTIAL PRIVACY IN LIFELONG MACHINE LEARNING

   Lai, Phung; Hu, Han; Phan, NhatHai; Jin, Ruoming; Thai, My T.; Chen, An M. (July 2022, 1st Conference on Lifelong Learning Agents)

   Precup, Doina; Chandar, Sarath; Pascanu, Razvan (Ed.)

   In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently bounded DP protection, given a growing stream of tasks. A consistently bounded DP means having only one fixed value of the DP privacy budget, regardless of the number of tasks. To preserve Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML with a streaming batch training, to efficiently train and continue releasing new versions of an L2M model, given the heterogeneity in terms of data sizes and the training order of tasks, without affecting DP protection of the private training set. An end-to-end theoretical analysis and thorough evaluations show that our mechanism is significantly better than baseline approaches in preserving Lifelong DP. The implementation of L2DP-ML is available at: https://github.com/haiphanNJIT/PrivateDeepLearning. 

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2. Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners

   Redberg, Rachel; Koskela, Antti; Wang, Yu-Xiang (December 2023, Advances in neural information processing systems)

   In the arena of privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) has outstripped the objective perturbation mechanism in popularity and interest. Though unrivaled in versatility, DP-SGD requires a non-trivial privacy overhead (for privately tuning the model’s hyperparameters) and a computational complexity which might be extravagant for simple models such as linear and logistic regression. This paper revamps the objective perturbation mechanism with tighter privacy analyses and new computational tools that boost it to perform competitively with DP-SGD on unconstrained convex generalized linear problems. 

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3. Privacy and Accuracy-Aware AI/ML Model Deduplication

   https://doi.org/10.1145/3725340  

   Guan, Hong; Yu, Lei; Zhou, Lixi; Xiong, Li; Chowdhury, Kanchan; Xie, Lulu; Xiao, Xusheng; Zou, Jia (June 2025, Proceedings of the ACM on Management of Data)

   With the growing adoption of privacy-preserving machine learning algorithms, such as Differentially Private Stochastic Gradient Descent (DP-SGD), training or fine-tuning models on private datasets has become increasingly prevalent. This shift has led to the need for models offering varying privacy guarantees and utility levels to satisfy diverse user requirements. Managing numerous versions of large models introduces significant operational challenges, including increased inference latency, higher resource consumption, and elevated costs. Model deduplication is a technique widely used by many model serving and database systems to support high-performance and low-cost inference queries and model diagnosis queries. However, none of the existing model deduplication works has considered privacy, leading to unbounded aggregation of privacy costs for certain deduplicated models and inefficiencies when applied to deduplicate DP-trained models. We formalize the problem of deduplicating DP-trained models for the first time and propose a novel privacy- and accuracy-aware deduplication mechanism to address the problem. We developed a greedy strategy to select and assign base models to target models to minimize storage and privacy costs. When deduplicating a target model, we dynamically schedule accuracy validations and apply the Sparse Vector Technique to reduce the privacy costs associated with private validation data. Compared to baselines, our approach improved the compression ratio by up to 35× for individual models (including large language models and vision transformers). We also observed up to 43× inference speedup due to the reduction of I/O operations. 

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4. Continual Learning with Differential Privacy

   https://doi.org/10.1007/978-3-030-92310-5_39  

   Desai, Pradnya; Lai, Phung; Phan, NhatHai; Thai, My T. (October 2021, International Conference on Neural Information Processing)

   In this paper, we focus on preserving differential privacy (DP) in continual learning (CL), in which we train ML models to learn a sequence of new tasks while memorizing previous tasks. We first introduce a notion of continual adjacent databases to bound the sensitivity of any data record participating in the training process of CL. Based upon that, we develop a new DP-preserving algorithm for CL with a data sampling strategy to quantify the privacy risk of training data in the well-known Averaged Gradient Episodic Memory (A-GEM) approach by applying a moments accountant. Our algorithm provides formal guarantees of privacy for data records across tasks in CL. Preliminary theoretical analysis and evaluations show that our mechanism tightens the privacy loss while maintaining a promising model utility. 

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5. DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy

   https://doi.org/10.1145/3448016.3457306  

   Wang, Chenghong; Bater, Johes; Nayak, Kartik; Machanavajjhala, Ashwin (June 2021, SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data)

   In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely outsource database computation, existing techniques do not consider the information that can be leaked via update patterns. To address this problem, we design a novel secure outsourced database framework for growing data, DP-Sync, which interoperate with a large class of existing encrypted databases and supports efficient updates while providing differentially-private guarantees for any single update. We demonstrate DP-Sync's practical feasibility in terms of performance and accuracy with extensive empirical evaluations on real world datasets. 

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