Top Technologies To Achieve Security And Privacy Of Sensitive Data In AI Models

Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy.

Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets. But researchers have found that you can extract personal information from anonymised datasets using alternate data, something known as linkage attacks.

As anonymised data is not good enough, other techniques have been increasingly utilised by companies to preserve privacy and security of data. In this article, we will take a look at them.

#opinions #data privacy #data security #differential privacy #ai

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Top Technologies To Achieve Security And Privacy Of Sensitive Data In AI Models

Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy.

Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets. But researchers have found that you can extract personal information from anonymised datasets using alternate data, something known as linkage attacks.

As anonymised data is not good enough, other techniques have been increasingly utilised by companies to preserve privacy and security of data. In this article, we will take a look at them.

#opinions #data privacy #data security #differential privacy #ai

Top Big Data Technologies Rising in 2021

Big Data applications are no longer a thing of the future – they are here and are steadily gaining steam globally. In this blog, we will explore different types of Big Data technologies and how they are driving success across industries.

Table of Contents

• Introduction to Big Data
• Demand for Big Data 
• Top 10 Big Data Technologies in 2021
• 1. Artificial Intelligence
• 2. SQL-based Technologies
• 3. R Programming
• 4. Data Lakes
• 5. Predictive Analytics
• 5. Hadoop
• 6. Apache Spark
• 7. Prescriptive Analytics
• 8. In-memory Database
• 9. Blockchain
• How to Upskill in Big Data?

Introduction to Big Data

In the digital era, businesses generate and encounter large quantities of data on an everyday basis. “Big Data” is essentially a term used to describe this massive collection of data that exponentially increases with time. It is now imperative for companies to adopt smart data management systems if they want to extract relevant information from the vast and diverse stockpile.

According to Gartner, Big Data has the following characteristics:

• It is high-volume and high-velocity.
• Contains a huge variety of information assets.
• Requires cost-effective and innovative forms of processing.
• Enhances decision making in organisations.

Today, we are witnessing a new crop of big data companies that are utilising emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to move beyond the conventional tools of management. Let us understand their reasons for doing so.

#big data #top big data technologies #top big data technologies rising in 2021 #technologies #echnologies rising #top big data technologies rising

Top 10 Trending Technologies Must Learn in 2021 | igmGuru

Technology has taken a place of more productiveness and give the best to the world. In the current situation, everything is done through the technical process, you don’t have to bother about doing task, everything will be done automatically.This is an article which has some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top trending technologies followed in 2021 and its impression in the coming future in the world.

1. Data Science
   First in the list of newest technologies is surprisingly Data Science. Data Science is the automation that helps to be reasonable for complicated data. The data is produces in a very large amount every day by several companies which comprise sales data, customer profile information, server data, business data, and financial structures. Almost all of the data which is in the form of big data is very indeterminate. The character of a data scientist is to convert the indeterminate datasets into determinate datasets. Then these structured data will examine to recognize trends and patterns. These trends and patterns are beneficial to understand the company’s business performance, customer retention, and how they can be enhanced.

2. DevOps
   Next one is DevOps, This technology is a mixture of two different things and they are development (Dev) and operations (Ops). This process and technology provide value to their customers in a continuous manner. This technology plays an important role in different aspects and they can be- IT operations, development, security, quality, and engineering to synchronize and cooperate to develop the best and more definitive products. By embracing a culture of DevOps with creative tools and techniques, because through that company will gain the capacity to preferable comeback to consumer requirement, expand the confidence in the request they construct, and accomplish business goals faster. This makes DevOps come into the top 10 trending technologies.

3. Machine learning
   Next one is Machine learning which is constantly established in all the categories of companies or industries, generating a high command for skilled professionals. The machine learning retailing business is looking forward to enlarging to $8.81 billion by 2022. Machine learning practices is basically use for data mining, data analytics, and pattern recognition. In today’s scenario, Machine learning has its own reputed place in the industry. This makes machine learning come into the top 10 trending technologies. Get the best machine learning course and make yourself future-ready.

To want to know more click on Top 10 Trending Technologies in 2021

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Smart Cities Raise Data Privacy Concerns

Technology is moving faster than most of us can comprehend and with the introduction of smart cities, controversial advancements are bound to affect us all at some point. Today nearly every part of our lives can be digitized, tracked, and logged. We are already globally connected through our smart devices such as smart TVs, smartwatches, and social media generating huge amounts of data every day. Even, our homes, cars are becoming smarter day by day. Any project or initiative that involves the collection of data raises the question of privacy.

So, should you be excited about smart cities or concerned about your privacy and data?

In this article, I examine three of the ten privacy principles (PIPEDA) formulated by the Office of the Privacy Commissioner of Canada; I discuss how these privacy principles can be violated under the new project gaining popularity known as “smart cities”.

Smart cities are a hot topic in Canada; many major cities like Toronto are looking into making cities more efficient through a system of connectivity known as IoT (Internet of Things). The World Bank defines smart cities as “a technology-intensive city, with sensors everywhere and highly efficient public services, thanks to the information that is gathered in real-time by thousands of interconnected devices.”

Although the goal of smart cities is to enhance the quality of life and provide a sustainable environment, when it comes to the question of privacy, even the people working under the smart cities project don’t seem to have a clear answer. With the rise of IoT devices in the cities, the most important question associated with privacy has to do with the safety of data collection. PIPEDA says, “the organization must have appropriate safeguards to protect the sensitivity of the collected personal information/data.”

Smart cities are not just about roads, parking meters, or better infrastructures; it is about the whole vast array of services. It is everything from digital signage, toll roads, and parking. It could also look like going into retail spaces and having the shops understand who you are, what you are wanting to buy, and would get you everything you need before you get there. This would mean that everybody knows who we are without having to say which begs the question of safety associated with data.

More IoT devices mean more data will be generated, including personal information, and hence more safety regulations and protection around processing the data will be required. As a smart city heavily relies on data generated through IoT devices, it makes the city vulnerable to cyber attacks and hackers posing a security threat to the citizens of the smart cities.

#privacy #technology #data #smart-cities #data-privacy #data-security #what-to-expect-from-smart-city #personal-data