Digitalization has made its way in the data-driven century. No matter how big or small the company, everyone faces the risk offered by Data breach.Hackers had enormous amounts of new ways in order to harvest companies personal data to fetch the money. Data breach occurs when hackers gain an access on the organization database resulting in loss of data like banking details, credit card infomartion.Data breach is the major problem in the world growing digitally.

The fiver major data breach in the history are listed the information is provided to gain knowledge and to avoid the same mistake as it affects several people

Yahoo

Date: 2013-2016

Impact: Over 3 billion user accounts exposed

The first data breach of yahoo took place in 2013 and the information provided that a group of russian hackers accessed the data of the account of more than billion users . Irrespective of the attack the deal with Verizon held. The resources of Verizon played a great role in enhancing the security of yahoo and it was in 2017 after Verizon bought yahoo the actual data of 3 billion was disclosed. These breaches caused Yahoo to knock $350 million off their sales price to Verizon

Linkedin

Date: June 2021

Impact: 700 million users

Professional networking site Linkedin faced the data breach affecting 700 million users however they argued having no personal and sensitive data was being exposed the God user posted data containing personal information including email addresses, phone numbers, geolocation records, genders and other social media details. A hacker going by the moniker of “God User” exploited the site’s rules. The breach provided a way to hackers to target profile companies.

Facebook

Date: April 2018

Impact: 50-90 million users exposed

In 2018, a British consulting firm, Cambridge Analytica hacked the data of facebook users and sold it. The loophole found in the API of Facebook found the former to compile the date.The company sold the data illegally for a long term as no security forms were implemented. It was when the Federal Trade Commission (FTC) found a charge for facebook violation and poor data practice things came to an end.

Microsoft

Date: January 2021

Impact: 30,000 US companies (60,000 companies worldwide)

One of the largest cyber crimes that took place in the history of the US by gaining unauthorized access to emails from small businesses to local governments.

eBay

Date: March 2014

Impact: 145 million users

Lack of communication and poor implementation of the password-renewal process are the two main factors which resulted in the successful data breach providing the hacker an access to the main network. The financial information is stored in a separate server and as a result there are no records of financial fraud. The hacker gained access to names, addresses, dates of birth and encrypted passwords.

Prompt-based learning is an emerging class of ML model training techniques. It allows users to specify the task they want the pre-trained language model to interpret and complete in natural language. It is in contrast to traditional transformer training methods, in which models are first pre-trained using unlabeled data and then fine-tuned for the desired downstream task using labeled data.

A prompt is a natural language instruction written by the user for the model to execute or complete.

Several prompts may be required, depending on the complexity of the trained task. Prompt engineering is the process of developing the best prompt, or series of prompts, for the desired use task. In natural language processing, supervised comprehension, training AI models on input data annotated for a specific output until they can detect the underlying relationships between the inputs and outputs plays a significant role (NLP). Researchers used domain knowledge to extract information from training datasets and provide models with the guidance needed to learn from the data in early NLP models.

However, with the introduction of neural network models for NLP, the emphasis shifted from feature engineering to model architecture engineering. Neural networks enabled features to be learned concurrently with model training.

A language model trained unsupervised—that is, on unlabeled data—can solve various tasks when given a set of carefully designed prompts. However, there is a catch: learning prompt-based necessitates the selection of the most appropriate prompt to allow a language model to solve the task at hand.

Prompt learning repurposes an AI model in what way?

The traditional pre-train, fine-tune paradigm has numerous advantages over prompt-based learning. The most significant advantage is that prompting works well with small amounts of labeled data. With GPT-3, for example, it is possible to achieve high performance on specific tasks with just one labeled example. Indeed, studies have shown that a single prompt can be as effective as training with 100 conventional data points. It suggests that prompting could significantly improve training efficiency, resulting in lower costs, less energy expended, and a faster time to value for AI models. For many businesses looking to leverage and train their NLP models, prompt-based learning is a prospect. However, there are some difficulties.

Prompt Learning Challenges

Prompts can be created either manually or automatically. But, creating the perfect prompt requires understanding a model’s inner workings and trial and error.

Before we understand the impacts of cloud computing on data storage, we must first understand what cloud computing is. Here is your explanation.

Cloud computing is the on-demand delivery of IT resources over the net with pay-as-you-go pricing. Rather than purchasing, holding, and maintaining physical databases and servers, you can access technology benefits, such as computing capacity, storage, and databases, on an as-needed basis from a cloud provider like AWS.

Alliances of every kind, dimension, and enterprise use the cloud for data backup, disaster retrieval, email, virtual desktops, software effect and testing, big data analytics, and customer-facing web applications.

The three main kinds of cloud computing are Infrastructure as a Service, Platform as a Service, and Software as a Service. Each kind of cloud computing delivers different power, flexibility, and leadership to choose the right benefits for your requirements.

Here are some impacts of cloud computing on businesses and firms.

• It delivers a method for companies to handle their resources online.

• It lets enterprise entities access their data effectively, anytime and anywhere.

• It provides scalability for customers to rise and down the resource requirements of the association.

• It delivers flexibility to the clients or users to choose the services they want to utilize and pay as per use.

• Users can hold more data on it than on their local physical storage appliances.

• It enables the comfort of working and has been boosting productivity.

• Cloud has brought a process in the entertainment industry. It uses the cloud to provide the content.

• Digital forms of patients are held in the cloud by Hospitals or Healthcare assistance providers.

• The knowledge, lessons, and material used for education are stored in the cloud. Now students can access asked data anytime, anywhere.

• It allows users to appreciate network-based contact tools such as emails, messaging, and calling apps like Gmail, WhatsApp, Google Duo, and Skype that are established on cloud infrastructure.

Mobile app development has come a long way since the early days of smartphones. There are over 5 billion mobile users worldwide, and mobile apps have become an essential part of our daily lives. As a result, mobile app development has become a highly competitive and constantly evolving industry.

Cross-platform development, AI and ML, IoT, cloud integration, and security are some of the latest trends in mobile app development. User-centered design performance, testing, ASO, and regular updates are the best practices for developing mobile apps that are user-friendly, efficient, and secure.

Let’s discuss the latest trends and best practices for developing mobile apps.

1. Cross-platform development Developing an app for multiple platforms (Android and iOS) is essential to reach a wider audience. Cross-platform development allows developers to write code once and deploy it on various platforms.

2. AI and Machine Learning Integrating Artificial Intelligence (AI) and Machine Learning (ML) technologies in mobile apps can provide a personalized experience to the user. Chatbots, voice assistants, and recommendation engines are some of the popular AI and ML applications in mobile apps.

3. Internet of Things (IoT) IoT has gained much attention lately, and its application in mobile app development is significant. Mobile apps can control smart homes, wearables, and other IoT devices.

4. Cloud Integration Cloud technology has revolutionized mobile apps’ ways that are developed and deployed. Cloud integration provides scalability, reliability, and security to mobile apps.

5. With data breaches, security, and privacy have become a top priority for mobile app developers. Developers must ensure the app is secure and the user’s data is protected.

Best Practices for Developing Mobile Apps

1. User-Centered Design: The app design should be user-centered, ensuring the app is easy to use and navigate.

2. Performance: The app should be optimized for performance, ensuring it loads quickly and runs smoothly.

3. Testing: Through thorough app testing, you can ensure it works seamlessly on different devices and operating systems.

4. App Store Optimization (ASO) App Store Optimization (ASO) is crucial to ensure that the app is visible to users in the app store.

5. Regular Updates: Regular updates to the app are essential to keep it relevant and competitive in the market.