Machine Learning Sets the Pace on Data Privacy
Benefits of Machine Learning and AI
Many tech specialists are touting the benefits of machine learning and artificial intelligence for business operations today and in the future. What are these benefits? How can these advanced forms of technology help business today, when many are struggling to stay in business at the height of a pandemic?
One of the significant areas in which machine learning, AI, and even deep learning applications can benefit businesses today is in taking control of their data. Data security and maintaining privacy compliance, along with protecting customer information, can make or break a company, even large enterprises. When it comes to cybersecurity, machine learning and artificial intelligence offer automation and efficiency that can’t be beaten for accuracy or accomplished with the same ease in any other method today.
The bottom line is that cybersecurity threats are on the rise, and a data breach can mean the loss of a company’s reputation, hefty fines, and possible complete detriment of customer faith and loyalty. A severe breach can force even the largest companies into bankruptcy.
AI, ML, and Deep Learning – What are They?
Not everyone is as tech-savvy as their employees that handle their data security, but having some basic background knowledge is essential to make the best decisions for your company that will help lead your business into the future – preferably ahead of your competition. For CEOs who are faced with sales calls from companies trying to sell this package or this particular feature can become stressful if the language is vague or the understanding is not clear. Artificial intelligence sounds excellent, but what exactly does it mean? What is machine learning and deep learning? How can these features be relevant to your company?
Video analytics has been the brass ring of computing for some time. We have been able to analyze number values and text quickly, but we have only just started reaching those thresholds to analyze video with the same speed and accuracy. For companies that handle a great deal of data in multiple formats, including video, knowing the best methods available today for controlling, storing, and analyzing that data is a game-changer.
Terms like machine learning, artificial intelligence, and deep learning sound impressive and innovative. Some companies often present their products with a lot of technical jargon, and often it can seem as if all three of these terms are interchangeable. When making major business decisions that can have a tremendous impact on your company’s future and security, it is good to know the difference and be sure that any product or software package purchased does what the salesperson states. These terms all have very different meanings and uses in the business world.
Let’s break these terms down very simply so that we know what we are working with and can make intelligent decisions without relying on a salesperson’s nod and a wink. Artificial intelligence, or AI, is referring to any type of machine intelligence. That’s it. The movie version of AI, with Will Smith, shows us an advanced form of machine intelligence. It can also apply to any simple application, even gaming software for simple children’s matching games—clearly, a broad spectrum. Just because someone is using terms like AI, it helps if they can narrowly define what their application can do for you and how it can benefit your business.
Machine learning (ML) is a product or branch of AI. In very basic terms, it means that your AI can learn new things. The AI built in the application has some form of essential cognitive ability. You should be able to feed it some type of data, and the application can learn to better perform based on the data you have given it. Machine learning uses algorithms to solve problems, and given new sets of data, it can use them to continue the basic principle, apply them and generate correct solutions.
Deep learning is a step above machine learning and advanced form of it. Deep learning is the step forward towards how humans think. As a more advanced form of machine learning, an application that uses deep learning is fed enormous amounts of data, sometimes data with noise, and it can learn classifications. The program can then take data and form classifications, groups, or patterns and make sound decisions based on real data sets. An example is recognizing human faces; not every face is identical. Still, as the program learns to form a classification or group to recognize the many varied forms of faces, it can then pick them out of photos or videos. This is how video analytics has become a possibility today.
By the Numbers – How AI & ML are Being Used
Businesses all over the globe have been incorporating both AI and ML in their technology for years. Studies have been done to discover just how they are using this technology and how often. These studies show a pattern of growth now, and one that will continue far into the future. A survey of over 800 IT cybersecurity professionals from around the globe indicated some of the following results.
- 96% report using AI and ML currently in their cybersecurity programs.
- Even though they are top in their fields, 7 out of 10 admit they are still unsure of what these terms even mean.
- 39% of these professionals report an increase in automation.
- 38% feel that AI and ML make them far more productive in their roles in cybersecurity.
- 37% have noticed a decline in human errors.
- 50% feel more confident that their data is more secure.
Regardless of the confusion on how AI and ML individually work to help their systems, overall, these technologists feel more confident in their roles and in doing their jobs. Now that companies are faced with lowered budgets and work-at-home employees, cybersecurity issues have come to the forefront for most companies. Solving these issues through intelligent automation makes sense.
How COVID19 is Accelerating Development
With a global pandemic having forced employees into a work-from-home situation, it increased stress on company tech infrastructures, created gaps in security, and sounded cybersecurity alarm bells. While most employees are still entirely trustworthy with company data, cybercriminals have upped their games. It is now more critical than ever for companies to gain the upper hand on their data, how it is distributed, and who has access.
Cybersecurity professionals are faced with the same health crisis as the rest of the world, and the number of these technologically savvy employees were already in short supply. Hence, advancements in automation are a welcome relief. It is expected that because of COVID19 pushing the progress of a work-at-home society, that it will ultimately create accelerated development of new AI and ML tools to correspond with the latest cyber threats of today.
Federated Machine Learning – A Privacy Revolution?
With the workforce expansion, federated machine learning may be the next privacy revolution breaking on today’s business horizons. Federated machine learning is a distributed machine learning method that was first introduced by Google approximately five years ago. This type of machine learning offers its ability to aggregate data from various sources, such as data inputs from multiple sources or locations. Before this technology, the requirement was for all pools of data to be housed in a centralized computer environment for source aggregation.
Privacy concerns are a priority. Workforces are dispersed. This creates a perfect opportunity for federated machine learning to take off and become the next revolution in data privacy management. Data sets can be pulled from multiple sources, including employee home computers. The data can be kept safe from cybercriminals at the same time as it is anonymized and scrubbed. The advancements are only just beginning and have come in a literal sense, just in time for the pandemic sweeping the globe.
Maintaining Secure Data Sets
In a movement to preserve consumer data privacy across platforms, finding ways to maintain secure data sets as information is transferred from one business to another through various encryption methods has become increasingly important. Casimir Wierzynski, a leader in the Privacy Preserving Machine Learning (PPML) movement, had this to say about why this is so important. “We’ve been looking at this collision course over the last couple of years around the need for data to train machine-learning systems to unlock all the power of AI but still keep data private. You’ll have one party that owns data, another party may own a model, and they’re running a system on somebody’s hardware.”
One new method being worked on by his team and Intel is homomorphic encryption (HE). This type of cryptography includes both a public and a private key, which allows applications to perform necessary functions on the data, without ever exposing the actual data itself. Wierzynski briefly described how it worked. “This is one of the most leading-edge techniques in this area. There are ways of doing math on the data while it stays encrypted, and the answer that comes out is still encrypted. It’s only the actual owner of the data who can reveal the answer.”
This may lead to a new era in private data. It gives back some of the control to the owner of the personal data or the consumer. Only they would have the key to understand the details, while there would be enough other information or data left for any functions required by the applications to do its job and perform tasks.