Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning (2207.12831v1)

Abstract: 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.

• The paper introduces Lifelong DP as a novel privacy guarantee that consistently bounds privacy loss across evolving tasks in continual learning.
• It proposes the L2DP-ML algorithm, which injects noise into inputs and hidden layers to maintain a constant privacy budget regardless of the number of tasks.
• Theoretical analysis and experiments on datasets like permuted MNIST and CIFAR-10 validate the approach, demonstrating competitive model utility with robust privacy protection.

Consistently Bounded Differential Privacy in Lifelong Learning

The paper "Consistently Bounded Differential Privacy in Lifelong Learning" addresses a critical and intricate challenge in the domain of continual learning—maintaining differential privacy (DP) as machine learning models evolve over time. As lifelong learning (L2M) systems continuously acquire new skills while retaining knowledge from earlier tasks, they must mitigate privacy risks associated with adversarial attacks on evolving model parameters. This research introduces a novel construct, Lifelong DP, which ensures consistent DP guarantees despite these challenges.

Key Contributions

1. Lifelong Differential Privacy (Lifelong DP): The paper defines Lifelong DP to safeguard the inclusion of any data tuple from multiple tasks within a lifelong learning framework under a consistently bounded DP guarantee. This notion is crucial given that traditional DP models often face unbounded privacy loss due to the ongoing and dynamic nature of task learning in L2M.
2. L2DP-ML Algorithm: To preserve Lifelong DP, the authors propose a scalable and heterogeneous algorithm, L2DP-ML. This algorithm incorporates mechanisms to introduce noise systematically into inputs and hidden layers. It effectively maintains a privacy budget invariant of the number of tasks—a significant advancement over baseline approaches that typically experience budget accumulation across tasks.
3. End-to-End Theoretical Analysis: The paper provides rigorous theoretical analysis demonstrating the efficacy of the proposed algorithm over conventional methods. The analysis justifies the algorithm’s ability to consistently maintain the Lifelong DP without increasing the privacy budget, irrespective of the number of tasks learned.
4. Experimental Validation: The research paper includes extensive experiments using datasets like permuted MNIST, CIFAR-10, and a unique dataset for human activity recognition. The results consistently show improved performance of the L2DP-ML algorithm in preserving DP while achieving competitive model utility compared to existing baselines.

Theoretical and Practical Implications

The formulation of Lifelong DP provides a robust theoretical framework for privacy-preserving mechanisms in continual learning systems. Practically, it paves the way for implementing DP in real-world systems where learning efficiency must be balanced with user privacy. This could revolutionize applications in sensitive domains such as healthcare and user profiling, where data privacy is paramount.

The paper also underscores the importance of handling the heterogeneity in data sizes and sequence of task learning. By integrating these factors, L2DP-ML demonstrates significant adaptability and computational efficiency in various operational environments.

Speculation on Future Developments

Future advancements may focus on reducing the computational cost and refining the balance between privacy and utility. The development of more sophisticated noise injection techniques or alternative definitions of neighboring databases may further enhance the robustness of Lifelong DP. Additionally, integrating machine learning with cryptographic techniques could provide even more potent defenses against privacy breaches in lifelong learning contexts.

In summary, the introduction of consistently bounded differential privacy for lifelong learning represents a significant conceptual leap, offering a solid foundation for ongoing research in secure and efficient continuous learning systems.