Harnessing AI's power without sacrificing data privacy
Privacy-preserving machine learning (PPML) integrated into AI has the potential to address data privacy concerns and manage data access.
Businesses are rapidly embracing new technology to make processes smoother and achieve faster delivery to customers. They are employing machine learning (ML) and artificial intelligence (AI) in various operations.
Machine learning, as a subset of AI, assists in online programs by utilising algorithms to perform specific tasks according to pre-altered instructions. However, some loopholes cause breaches, giving rise to information and privacy concerns. Majorly, this affects most internet business websites, where customers have to add their personal details.
As per a 2021 annual report, the Indian Computer Emergency Response Team (Cert-In) said it managed a total of 1,402,809 incidents, including data breaches. The Indian government is continuously striving to enhance online security and safeguard users by providing safe and secure online platforms.
Data-related threats faced by businesses
Modern businesses encounter a range of threats that imperil their data integrity and customer privacy. These threats include insider risks, accidental data exposure, and a particularly concerning issue—ransomware.
The harmful software targets corporate devices, encrypting valuable data until a decryption key is provided. This menace can also spread across the corporate system, amplifying the danger.
The consequences of these threats go beyond breached customer privacy—they can trigger email fraud and financial scams, eroding the credibility of a business's online presence. It also compromises customer trust and raises concerns about the authenticity and privacy of business websites.
How can privacy-preserving ML ensure data privacy?
With technology progressing at a fast pace and an increasing demand for individuals to provide their personal information, privacy concerns are heightened. Nevertheless, privacy-preserving machine learning (PPML) integrated into AI has the potential to address data privacy concerns and manage data access.
PPML serves to enhance the security of sensitive information and safeguard the confidentiality and privacy of customers and businesses, ensuring safe and secure data utilisation.
- Data privacy during the model training process: As businesses establish their ML models, PPML plays a pivotal role in safeguarding sensitive information from unauthorised access. Employing techniques like data masking, data anonymization, synthetic data generation, and homomorphic encryption—PPML strengthens the protection of vital data throughout the procedure, thereby empowering the creation of precise and impactful models.
- Manages input and output data: Through PPML, businesses can establish a secure framework safeguarding customer information, granting access exclusively to the enterprise and the customer. The approach prevents any sharing of data with third parties, ensuring the business models solely utilise and access first-party data.
PPML techniques to be used with AI
- Homomorphic encryption: Homomorphic encryption allows businesses to use the encrypted data securely without exposing it. It allows business operations to run smoothly without using traditional ways to use encrypted data by decrypting it, which is time-consuming and leads to security breaches.
- Federated learning: Federated learning empowers businesses to safeguard customer data and privacy without requiring central sharing. The model operates on individual devices, enabling real-time personalisation for improved recommendation accuracy—all while upholding privacy considerations.
- Differential privacy: This approach enables businesses to selectively access specific data instead of collecting all sensitive information. By applying noise to the data, it promotes transparency between customers and businesses. Customers are informed about the data used and the reasons for its use. Additionally, customers have the option to decline to share their information if they do not agree, maintaining their control over their data.
- Data masking and anonymization: Data masking conceals authentic data using realistic yet fabricated information to safeguard sensitive details while maintaining data functionality. Anonymization alters data to prevent its association with individuals, ensuring privacy while enabling analysis. Both strategies are essential for protecting sensitive information and establishing customer trust in the enterprise through transparent communication and providing customers control over their data.
- Synthetic data generation: While ML training, rather than using the original data, businesses can use artificial data to help preserve customer’s data and privacy without revealing sensitive information.
These techniques contribute to data privacy without impeding any processes, proactively addressing security concerns. As a result, businesses and customers can confidently leverage AI, ensuring that sensitive data breaches are avoided, bolstering trust and maintaining confidentiality.
The integration of PPML in AI empowers businesses to leverage AI's potential while safeguarding data privacy. As businesses adopt AI and ML to streamline operations, concerns over data breaches grow, especially for online businesses collecting customer data.
PPML addresses such challenges by employing these techniques. These measures prioritise data security without hindering processes, allowing AI to be used confidently while upholding customer trust and confidentiality.
Anil Bains is the Founder and CEO of Attryb.
Edited by Suman Singh
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)