Differential privacy: The white noise of cybersecurity

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  Quantumrun Foresight
• December 17, 2021

Insight summary

Differential privacy, a method that introduces a level of uncertainty to protect user data, is transforming the way data is handled across various sectors. This approach allows for the extraction of essential information without compromising personal details, leading to a potential shift in data ownership where individuals have more control over their information. The adoption of differential privacy could have wide-ranging implications, from reshaping legislation and promoting fair representation in data-driven decisions, to stimulating innovation in data science and creating new opportunities in cybersecurity.

Differential privacy context

Current infrastructures run on big data, which are large sets of data used by governments, academic researchers, and data analysts to discover patterns that will help them in strategic decision-making. However, the systems rarely take into account the potential hazards for users’ privacy and protection. For example, major tech companies like Facebook, Google, Apple, and Amazon are known for data breaches that can have harmful consequences on user data in multiple settings, such as hospitals, banks, and government organizations. 

For these reasons, computer scientists are focusing on developing a new system for storing data that does not breach user privacy. Differential privacy is a new method of protecting user data stored on the internet. It works by introducing certain levels of distraction or white noise into the data collection process, preventing accurate tracking of a user’s data. That approach provides corporations with all the essential data without revealing personal information.

The math for differential privacy has been around since the 2010s, and Apple and Google have already adopted this method in recent years. Scientists train algorithms to add a known percentage of incorrect probability to the data set so that no one can trace information to a user. Then, an algorithm can easily subtract the probability to obtain the actual data while maintaining user anonymity. Manufacturers can either install local differential privacy into a user’s device or add it as centralized differential privacy after collecting data. However, centralized differential privacy is still at risk of breaches at the source. 

Disruptive impact

As more people become aware of differential privacy, they may demand more control over their data, leading to a shift in how tech companies handle user information. For instance, individuals may have the option to adjust the level of privacy they want for their data, allowing them to balance between personalized services and privacy. This trend could lead to a new era of data ownership, where individuals have a say in how their data is used, fostering a sense of trust and security in the digital world.

As consumers become more privacy-conscious, businesses that prioritize data protection could attract more customers. However, this also means that companies will need to invest in developing differential privacy systems, which could be a significant undertaking. Furthermore, companies might need to navigate the complex landscape of international privacy laws, which could lead to the development of flexible privacy models adaptable to various jurisdictions.

On the government side, differential privacy could revolutionize how public data is handled. For instance, the use of differential privacy in census data collection could ensure the privacy of citizens while still providing accurate statistical data for policy-making. However, governments may need to establish clear regulations and standards for differential privacy to ensure its proper implementation. This development could lead to a more privacy-focused approach to public data management, promoting transparency and trust between citizens and their respective governments. 

Implications of differential privacy

Wider implications of differential privacy may include: 

• A lack of specific user data discouraging companies from tracking it and leading to a reduction in the use of targeted advertisements on social media and search engines.
• Creating a broader job market for cybersecurity advocates and experts. 
• A lack of data available for law enforcement agencies to track criminals leading to slower arrests. 
• New legislation leading to more stringent data protection laws and potentially reshaping the relationship between governments, corporations, and citizens.
• Fair representation of all groups in data-driven decision-making, leading to more equitable policies and services.
• Innovation in data science and machine learning leading to the development of new algorithms and techniques that can learn from data without compromising privacy.

Questions to consider

• Do you think major tech corporations can fully incorporate differential privacy into their business models? 
• Do you believe hackers will eventually be able to surpass novel differential privacy barriers to access target data?