Introduction
The growing prevalence of Chatbots, like the widely recognized ChatGPT, has completely transformed human-machine interactions. These language models powered by AI present a seamless and human-sounding conversational interaction. Increasingly widespread in different sectors like customer service, healthcare, finance, and others. However, as the adoption of ChatGPT rises, the associated privacy threats also escalate.
The Rising Popularity of ChatGPT and Privacy Concerns
Due to the growing adoption of ChatGPT across diverse sectors, the compiling of user queries is now regarded as an invaluable data treasure. Unluckily, this has created anxiety concerning the potential utilization of this data without users’ total awareness. Even though users might be careful about disclosing explicit Personally Identifiable Information (PII), the inherent privacy vulnerabilities of using natural language queries represent a notable risk.
Use-case 1: Sentiment Analysis and Dynamic Pricing
Understanding emotions through NLP is a significant use case, This enables ChatGPT to comprehend user emotions and provide fitting responses. While this is beneficial for customer support bots, it could also be manipulated in e-commerce situations. To illustrate, if someone shows great excitement while inquiring about a product, it could result in receiving an inflated price quotation., exploiting the user’s enthusiasm.
Use-case 2: Location Queries and Unintended Disclosures
Many chatbot interactions involve queries related to location, helping users find services or products nearby. Nevertheless, this creates a privacy vulnerability as users unknowingly disclose their location, Moreover, the bot may not be deployed or designed for that particular area. Such unintended disclosures can be exploited by malicious entities.
The Hypothesis: Human-like Conversations and User Vulnerability
Users have transitioned from relying on keywords for search to using natural language queries, resulting in interactions with chatbots that resemble conversations with actual people. Many users tend to give more context when asking their questions, making them susceptible to privacy infringements, because confidential information might accidentally get disclosed.
Introducing a new module called Privacy Preserving Chat (PPC)
To resolve these privacy concerns, a proposed solution is the Privacy Preserving Chat Module (PPCM). It serves as a middleman connecting the user and the backend NLP engine, employing filtering and transformation methods to secure sensitive information.
Filtering: Protecting Sensitive Information
In Use-case 2, where location information may be inadvertently shared, sensitive entities like locations are identified by the PPCM through the utilization of text extraction algorithms. Subsequently, the query is filtered or deleted before reaching the backend NLP engine, keeping user’s privacy secure.
Transformation: Anonymizing User Queries
In order to overcome the pricing disadvantage emphasized in Use-case 1, the PPCM applies transformation techniques to adjust the user’s original query and provide a more impartial response with synonymous semantics. Concerning requests about specific locations, applying abstraction is a possibility, preserving the accuracy of the user’s location and delivering beneficial answers.
Conclusion: Safeguarding User Privacy in ChatGPT
Convenience and efficiency have been introduced to different industries thanks to the rising popularity of ChatGPT., However, it also gives rise to significant privacy concerns.. Implicit privacy risks stemming from natural language queries necessitate thoughtful approaches to protect user data. The PPCM comes with a potential solution, preserving, preserving the benefits of ChatGPT while safeguarding user privacy. As artificial intelligence technology keeps progressing, crucial aspects involve implementing responsibly and respecting user privacy to ensure a secure and respectful user experience.
