GPT-4 Development and the Big Challenges of Data Privacy

On Wednesday, ChatGPT version 4.0 (referred to as GPT-4) was officially released, igniting discussions in our circle. GPT-4 dominated the headlines in various channels and communities, whether in academia or industry, as a topic of conversation over tea or as a source of innovation for brainstorming. Everywhere, we could hear the shock that "GPT-4" brought us, as if it were just like when ChatGPT was first released.

GPT-4 Development and the Big Challenges of Data Privacy

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However, amidst the celebration, we also need to think calmly, especially from the perspective of privacy protection when it comes to GPT-4.

The enormous advantage of GPT-4 in generating content is accompanied by potential problems. 

As a powerful new tool, GPT-4 has the potential to completely change the way some companies and organizations create and distribute content. The advantages of using GPT-4 to generate content are obvious. Firstly, it is very efficient and can quickly generate content on a large scale. In addition, it can create customized content for specific audiences, allowing companies to tailor content to different groups of people and achieve personalization. GPT-4 can also improve attractiveness and readability, making the output content more appealing and readable.

However, using GPT-4 to generate content also poses some potential problems. Firstly, the generated content may be considered too generic or not customized enough for specific audiences. After all, relying solely on data research of users is not enough, and the user's environment must be fully considered. This means that companies may need to invest additional resources to edit and modify content. But this will be more difficult than traditional editing because we do not understand the original thinking behind the writing or generation.

Finally, inevitably, the generated content may contain biases, such as misleading or even discriminatory content. For example, being asked to judge whether someone is a good scientist based on their race and gender clearly favors white men. This bias may be due to the lack of data filtering and can lead to serious ethical issues.

GPT-4 always faces the challenge of protecting data privacy. 

While there have been significant upgrades in its capabilities, users' concerns about data privacy have not disappeared. One major reason is that the CPT-4 model relies on training with large datasets.

The GPT-4 model relies on a large amount of personal data for training.

The GPT-4 model is trained using large conversation datasets collected from various sources, including social media, public forums, and other channels that are currently unknown. This means that the model is constantly exposed to various conversations, which may contain sensitive information about individuals. Each upgrade may come with new conversations and new privacy risks. To protect user data security, developers must take measures to ensure that the data used to train GPT-4 is properly protected. One common measure is to encrypt the data and ensure that only authorized personnel can access it. In addition, developers should consider using data masking and anonymization, token mechanisms, and other measures to further protect user data. At the same time, relevant departments need to establish policy standards to clarify the use of GPT-4 and how to handle user data.

Enterprise organizations are also concerned about the security risks posed by GPT-4.

Most enterprise organizations need to consider data security when handling data, so whether enterprise data can be accessed by GPT-4 is a key issue. Some companies are updating their privacy policies to restrict employees from using GPT-4 when handling work to protect their intellectual property and privacy information. Although OpenAI's privacy is protected by relevant terms, the security of users' personal information has not received enough attention. According to the frequently asked questions about GPT-4, conversations will be monitored to improve the system and comply with its policies and security requirements. However, this does not guarantee that user data will be absolutely secure. At least for now, GPT-4 does not provide options for users to delete personal information collected by AI models. This means that all data security and privacy obligations are the responsibility of the user, not the platform.

The national security issues brought by cross-border data flow need to be taken seriously.

In 2022, the 26th meeting of the Central Comprehensive Deepening Reform Committee passed the "Opinions on Building a Data Basic System to Better Play the Role of Data Elements", pointing out that data as a new production factor has rapidly integrated into various fields and has become one of the five major production factors in China. However, cross-border data flow also brings security risks, especially when it involves personal information, sensitive information, and national security. As a generative artificial intelligence, GPT-4 has the ability to collect, store, and use massive data. In human-machine interaction and question-and-answer, it may involve personal information, trade secrets, and other data, and there are security risks in cross-border data flow. China's "Cybersecurity Law", "Data Security Law", "Personal Information Protection Law", and other laws also need to be complied with regarding data export.

Therefore, we really need to ask, has the data used to train the GPT-4 model really been collected with the user's consent? What measures has GPT-4 taken to address user data security issues in the training and application of the model? To what extent has GPT-4 achieved privacy protection? We look forward to OpenAI providing us with answers in future versions.

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