MIT News Presents a Fresh Perspective on Data Privacy
Introduction:
A team of researchers at MIT has developed a groundbreaking technique that can protect sensitive data in machine-learning models. The technique, called Probably Approximately Correct (PAC) Privacy, allows researchers to add the minimal amount of noise required to protect the data while maintaining accuracy. Unlike other approaches, PAC Privacy does not require knowledge of the model’s inner workings or training process. The researchers created an algorithm that determines the optimal amount of noise based on the uncertainty of the sensitive data. This automatic technique guarantees privacy, even against adversaries with infinite computing power. PAC Privacy has the potential to revolutionize data privacy in machine-learning models.
Full Article: MIT News Presents a Fresh Perspective on Data Privacy
MIT Researchers Develop Technique for Protecting Sensitive Data in Machine Learning Models
Researchers at MIT have developed a new technique that addresses the problem of protecting sensitive data in machine learning models. The team created a privacy metric called Probably Approximately Correct (PAC) Privacy and built a framework based on this metric that can automatically determine the minimal amount of noise needed to protect the data. This framework is applicable to different types of models and applications and does not require knowledge of the model’s inner workings or training process. The researchers found that PAC Privacy requires less noise compared to other approaches, making it possible to hide training data while maintaining accuracy in real-world settings.
Defining Privacy Using PAC Privacy
The researchers introduced the PAC Privacy privacy metric that focuses on the difficulty an adversary would face in reconstructing parts of the sensitive data after noise has been added. This differs from traditional methods, such as Differential Privacy, which aim to prevent an adversary from distinguishing data usage but often require large amounts of noise. PAC Privacy takes into account the uncertainty or entropy of the data and determines how much noise must be added based on variance analysis.
Algorithm Advantages and Limitations
The PAC Privacy algorithm offers several advantages over other privacy approaches. It does not require knowledge of the model’s inner workings or training process, making it easier to implement. Additionally, users can specify their desired level of confidence, and the algorithm will determine the optimal amount of noise to achieve that goal. However, PAC Privacy does not inform users of the accuracy loss that will occur once noise is added. Furthermore, the technique can be computationally expensive as it involves training the model on multiple subsampled datasets.
Future Improvements and Impact
To improve PAC Privacy, the researchers suggest modifying the machine-learning training process to create more stable models. Stabler models would give smaller variances between subsample outputs, reducing the number of times the algorithm needs to be run and the amount of noise to be added. The researchers plan to explore the relationship between stability and privacy, as well as privacy and generalization error, in the future. The empirical approach of PAC Privacy broadens its reach to a variety of data-consuming applications.
Industry Perspective
Jeremy Goodsitt, senior machine learning engineer at Capital One, commended the PAC Privacy technique for reducing the amount of added noise while maintaining privacy guarantees. He stated that this solution offers broader applications and acknowledges the trade-off between privacy and utility.
Funding for this research was provided by DSTA Singapore, Cisco Systems, Capital One, and a MathWorks Fellowship. The research will be presented at the International Cryptography Conference (Crypto 2023).
Summary: MIT News Presents a Fresh Perspective on Data Privacy
Researchers at MIT have developed a technique called Probably Approximately Correct (PAC) Privacy that can determine the minimal amount of noise needed to protect sensitive data in machine learning models. PAC Privacy uses a new privacy metric and framework to automatically add the smallest amount of noise possible without sacrificing accuracy. Unlike other approaches, PAC Privacy does not require knowledge of the inner workings of the model or its training process. The algorithm relies on uncertainty in the original data and can guarantee privacy even against adversaries with infinite computing power. The technique has the potential to greatly improve privacy protections in machine learning models.
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