The battle for future markets and bigger market shares is very common these days. Most of the highly influential companies are in a race for developing better-automated systems and boosting artificial intelligence technology by taking them ahead in terms of competition. However, the development of machine learning and artificial intelligence technologies seems to be disturbed by a major obstacle that is data privacy.
Artificial Intelligence and machine learning systems automate repetitive tasks through the input of a huge amount of data. The more data is consumed, the better computer algorithms can recognise and capture patterns in the data. Hence, Artificial Intelligence need excessive amounts of data to find out the structure of the data then learn it automatically and predict the next step. The more data you feed, the more accuracy you get. However, it’s been unclear how these algorithms evolve and how they interconnect, possibly by processing customer data more than what was actually planned.
On the other hand, data protection is based on minimization, transparency, alteration and deletion of customer data. In such a case, big data usage can actually disturb the data privacy.
FEDERATED LEARNING, DECENTRALISATION OF DATA
Thinking of how machines learn from data, one of the most important technical necessity is the centralization of data. The strategy is basically to build a model on the basis of a given set of data in a closed space like a cloud or a data centre, where the data can be integrated, used, and controlled but cannot leave this defined space. This space or platform will not ensure that the data is saved and used only for some right purpose without falling into the wrong hands.
This is where federated learning comes into play. Therefore, in order to solve security issues caused by data centralisation, federated learning helps in securing data by decentralizing it. The developers would receive an anonymised customer data without any specific data which cannot trace a particular user. Thus, rather than storing and analysing the data on a centralised and possibly insecure platform, data can be used locally on the user’s device or the server and only the learning outcome is transferred and centralised.
This type of machine learning enables phones or computers to do predictive model training while keeping the entire data on the device itself, thereby reducing the need to transfer and store the data in the cloud or on a data centre. It enables machines to learn from a huge amount of data without centralising the data or risking the tracing sensitive and private information of a customer.
AI systems are being designed to fight cyber threats by outwitting them. Many researchers are adding a process known as Attack, Detect, and Protect in order to protect their AI systems and applications. This includes the usage of-
- Facial recognition
- Medical data
- And other methods of identifying people
They are also able to model a potential hacker, simulating attacks and creating counter-measures prior to the attacks.
Unfortunately, the hackers are also the experts. They have a variety of attack methods and different ways of using Artificial Intelligence to their advantage. For example- Evasion attacks. In this case, the system is flooded with false negatives which cause security analysts to ignore alerts.
Poisoning attacks is another example of injecting false data which is designed to poison the AI training data. This kind of attack can change the AI model significantly by impacting its decisions and outcomes. Unfortunately, hackers can use their own AI and send it crawling through the internet, searching for vulnerabilities.
For many internet businesses, the goal is to analyse incoming data using relationships and not just similarities. Machine Learning can provide ways to achieve this goal, while simultaneously supporting privacy and data protection. ML systems which contain personal data must be able to locate information, alter it, and limit what is done with it.
Additionally, there is the diversity of mobile phones and applications to deal with all of these. Technically speaking, it can be difficult. The more connections a system contains, the more the potential for security threats, which means the demands on security are becoming increasingly complex and may extend to devices not yet included in a data security program.
The GDPR complicates the process by requiring transparency and minimising the amounts and kinds of data that can be collected. The GDPR states:
- When an organisation collects personal data, it must state what the collected data will be used for. The data cannot be used for any other purpose, which includes sharing it with third parties.
- Only the minimum amount of data needed for a project or process is to be collected. Data can only be held for a limited time.
- An organisation must tell people what data about them it has and what is being done with it.
- An organisation must alter or eliminate an individual’s personal data, if requested.
- When personal data is being used for automated decisions about people, the organisation must be able to explain the logic behind the decision-making process.
WHY IT MATTERS?
The digital business persons have developed a very specific corporate culture with a research-driven technique based on complete data-supported decision-making and management. With digital transformation, the amount and variety of data are immensely increasing. Companies aiming to increase their market share are dependent on this customer data and its efficient and safe use.
The disadvantages implied by storing and using centralised data have already caused many data scandals. These have cast a bad image on many influential companies and global corporations by substantially hampering their brand reputation and destroying their market share. In addition to that, data privacy breach scandals, other cyber-attacks have caused a big fear of data loss and exposure.
Data security can make a business and even break the business, but federated learning with decentralised data can be an approach for effectively increasing a company’s profitability by using machine learning technologies while ensuring secure usage of customer data.
Sumit Kumar, Co-Founder & CEO, Gravitas AI