Posted by Tyler Chancey, GCFA on

Tyler Chancey is a cybersecurity professional currently serving as the Director of Cyber Security at Scarlett Cybersecurity Services. With a solid foundation in Computer Software Engineering from the University of Florida, Tyler holds a repertoire of certifications that underscore his expertise. These include the prestigious Microsoft 365 Certified: Enterprise Administrator Expert and Microsoft 365 Certified: Security Administrator Associate, showcasing his mastery in Microsoft's enterprise solutions. Tyler's commitment to comprehensive security is further evidenced by his CompTIA Security+ certification, demonstrating proficiency in core cybersecurity principles. Additionally, his GIAC Certified Forensic Analyst (GCFA) credential attests to his advanced skills in forensic analysis—an invaluable asset in today's complex cybersecurity landscape. Tyler's dedication to staying at the forefront of industry standards is evident in the active pursuit and maintenance of these certifications, making him a trusted authority in the field.

Tyler C., GCFA 

Job title: Director of Cyber Security
Expertise: Information Security, Cybersecurity Incident Response, Cybersecurity Compliance, Cyber Policy
Education: University of Florida, Computer Software Engineering

Highlights:

  • Director of Cyber Security at Scarlett Group since 2022 
  • Holds GCFA and Microsoft 365 Enterprise Administrator certifications
  • Expertise in compliance, incident response and cyber policy

Experience: 

Tyler C. currently serves as the Director of Cyber Security at Scarlett Group in Jacksonville, Florida. He first joined Scarlett Group in 2019 as a Cyber Security Consultant, before being promoted to his current director role in 2022. Tyler has over 4 years of experience providing cybersecurity services to American private and public organizations.

Education:

Tyler earned his degree in Computer Software Engineering from the University of Florida in 2016. While at UF, he developed expertise in programming and software development.

Licenses & Certifications:

  • Microsoft 365 Certified: Enterprise Administrator Expert (Issued May 2023)
  • GIAC Certified Forensic Analyst (GCFA) (Issued Jan 2019, Expires Jan 2027)  
  • Microsoft 365 Certified: Security Administrator Associate (Issued Jul 2022, Expired Jul 2023)
  • CompTIA Security+ (Issued Jun 2020, Expired Jun 2023)

Additional Skills: 

  • Customer Service, Leadership, Public Speaking, Network Security, Forensic Analysis, Disaster Recovery, Cloud Applications

Big data has become an important part of our everyday lives. Organizations of all kinds and fields are now collecting and analyzing huge amounts of data to learn more about their customers and make better decisions. Big data poses various concerns, notably privacy, and data exploitation.

Big data privacy concerns refer to the threats and risks associated with collecting, storing, analyzing, and using vast amounts of personal information in the era of data-driven decision-making. It highlights the need for strong security, transparency, informed permission, and ethical data processing to protect privacy rights.

92% of Americans are concerned about their privacy when using the Internet. 44% of data breaches involve customer data. 67% of Americans know their country's rules about privacy and protecting data. 76% of people think companies should protect their online information more. 58% of users said they would be willing to share data to avoid paying for online content.

This article will explore the most prominent privacy issues associated with big data and explain why organizations should consider utilizing cybersecurity services provided by reputable cybersecurity providers.

What is Big Data?

Big data refers to large and complex data sets that cannot be effectively managed, processed, or analyzed using traditional data processing methods. It includes many structured, semi-structured, and unstructured data from mobile devices, sensors, social media, etc. The three Vs that describe big data are volume, velocity, and variety.

Volume

Big data involves enormous amounts that surpass traditional databases' storage and processing capabilities. It includes data in terabytes, petabytes, or even exabytes.

Velocity:

Big data is generated and collected at an unprecedented speed. Data streams, including social media postings, website logs, and sensor data, need speedy processing and analysis.

Variety:

Big data includes many different types and forms of data, such as structured data (like relational databases), semi-structured data (like XML files), and unstructured data (like text, pictures, and videos). It includes data from multiple internal and external sources of an organization.

Dimensions of Big Data

This table highlights some of the most common high volume, high velocity, and wide variety datasets that modern organizations utilize for insights and decision-making. 
DimensionDescriptionExamples
VolumeLarge amounts of data generated and stored- Weblogs: contain info about visitors and can grow exponentially
- Social media content: text, images, video
- Purchase transaction records
VelocitySpeed at which data is generated and processed to meet demands- Live stock ticker data for financial services
- Sensor data from industrial equipment 
- Clickstream data
VarietyDiverse data types from various sources- Social media posts
- Satellite images
- Biomedical research data
- Mobile activity: call logs, GPS data

 

Impact of Big Data on Privacy

Big data includes vast amounts of information from various sources, including personal data such as lifestyles, online behavior, and health records. Collection and analysis of these data sets can lead to unprecedented insights. However, it also poses risks to individual privacy.

Types of Big Data

Big data can be classified into different types based on its source, format, and nature. Organizations and researchers must understand these types to use the power of big data and make effective choices. Let's explore some of the types of big data:

Structured Data

This data type is very well organized and follows a set style, like data in a database or a spreadsheet. Structured data can be easily analyzed using traditional data processing methods.

Unstructured Data

Unstructured data requires a specific framework or organization, making examining it easier. Examples include text documents, images, videos, social media posts, and sensor data. Getting useful information from unorganized data needs advanced methods like natural language processing and machine learning.

Semi-Structured Data

Semi-structured data falls between structured and unstructured data. It possesses some organizational properties but does not conform to a rigid structure. Examples include XML files, JSON data, and log files.

Time-Series Data

Time-series data comprises data points collected over time at regular intervals. It is often used for financial research, predicting the weather, and monitoring how well a system works.

Geospatial Data

Geospatial data includes data tied to a specific geographical area. It can come from many places, like GPS devices, satellites, and geotagged social media posts. Analyzing location data can help with urban planning, transportation efficiency, and environmental monitoring applications.

National Library of Medicine (NLM) provides a vast amount of biomedical literature and research data through its PubMed Central (PMC) platform. It demonstrates, how big data is being utilized in the healthcare domain and showcases the role of institutions like the NLM. They provide valuable resources for researches and practitioners using all these types of big data.

Use of Big Data

Use of Big Data

The assessment and utilization of big data offer many possibilities to gain valuable insights, make better choices, and drive innovation. By using big data, companies can find patterns, trends, and connections that can help them be more efficient, give customers a better experience, and make better business plans.

Business Intelligence and Analytics

Big data is key to getting ideas and making choices based on data. Organizations can find trends, patterns, and connections in big datasets to improve processes, customer experience, and innovation. Big data analytics helps businesses understand customer behavior, improve pricing strategies, segment the market, and predict future trends.

BIg data analytics can help identify patterns in patient data that indicate a higher risk of developing chronic conditions like diabetes or heart disease. This type of information can enable healthcare providers to proactively intervene and implement preventive measures to improve patient health outcomes. 

ASPE report provides a comprehensive overview of the potential appliactions big data across various sectors, including healthcare.

Machine Learning (ML) and Artificial Intelligence (AI)

ML and AI systems need big data. Complex models can be trained to recognize patterns, make predictions, automate processes, and improve system performance using abundant data. Big data-driven ML and AI applications are employed in healthcare, banking, e-commerce, and recommendation systems.

Research and Development

Big data has become an invaluable asset in scientific research and development. Researchers use big data sets to learn about inheritance, climate science, physics, and the social sciences, run models, and make discoveries. Big data assists in identifying correlations, understanding complexities, and driving innovation.

Internet to Things (IoT)

The rapid growth of IoT devices generates massive amounts of data. Big data technologies offer real-time monitoring, predictive maintenance, and intelligent decision-making by storing, processing, and analyzing IoT data. IoT applications in urban areas, connected vehicles, healthcare monitoring, and industrial automation utilize big data for effective implementation.

Risk Analysis and Fraud Implementation

Big data analytics helps organizations identify and mitigate risks while combating fraud. Businesses may minimize losses and secure their systems and consumers by examining data sources, including financial transactions, user behavior, and historical trends.

Privacy Concerns in Big Data

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Privacy concerns in big data are significant and have gained widespread attention recently. Here are some key privacy concerns associated with big data.

Big Data Privacy Risks Across Data Lifecycle

This table outlines prominent privacy risks such as unauthorized access, identification, profiling and discrimination across key stages of the big data lifecycle like collection, storage, processing.
StageRisksExamples
Collection- Unauthorized access
- Reidentification
- Collecting personal data without consent 
- Combining anonymized datasets to identify individuals
Storage- Data breaches
- Unauthorized access
- Unencrypted databases accessed by hackers 
- Insider data theft
Processing- Profiling 
- Discrimination
- Algorithmic biases leading to unfair outcomes 
- Targeted advertising without consent
Sharing- Unauthorized dissemination 
- Data trafficking
- Users' personal information sold without permission 
- Third party data sharing without consent
Analysis- Surveillance 
- Manipulation
- Tracking individual behavior online/offline 
- Distorting information to influence opinions

Data Collection

Big data usually involves gathering personal information from different places, like social media, online interactions, and sensor data. People should be worried when their information is collected without their knowledge or permission or when it is taken for one reason and then used for something else.

Data Breaches - Obstruction of Privacy

Big data archives can become attractive targets for hackers due to the valuable and sensitive information they hold. A single breach can expose a huge amount of personal data, leading to identity theft, financial fraud, or other bad things.

Identification and Re-identification

Big data analytics can identify individuals by combining seemingly anonymous data from multiple sources. Even if personal details are deleted, finding out who someone is may still be possible by combining data points or using information from outside the system. This poses a threat to individuals' anonymity and confidentiality.

Profiling and Discrimination

Big data analytics can create detailed profiles of individuals based on their behaviors, preferences, and characteristics. This profiling can lead to discriminatory practices, such as differential pricing, employment bias, or unfair targeting of certain groups.

Lack of Control

People often need more control over their data once it becomes part of "big data." They may need to be made aware of what data is being collected, how it is being used, and with whom it is being shared. This lack of power makes it harder for people to keep their information safe.

Consent and Transparency

Obtaining meaningful consent from individuals becomes challenging in big data, where data is collected from various sources and used for diverse purposes. It is crucial to ensure transparent data practices, informing individuals about data collection, usage, and potential risks.

Data Governance and Accountability

Big data ecosystems require multiple stakeholders, including collectors, processors, and third-party analytics providers. Privacy is protected by ensuring these groups have good data control and responsibility. Clear regulations and guidelines are needed to establish responsible data practices.

Informed Consent and Notice

When dealing with diverse data sources, obtaining informed consent and providing clear notice to individuals about collecting and using their data becomes complex.

Third-Party Data Sharing

Sharing data with third parties, such as business partners or data brokers, introduces additional privacy concerns. To protect personal information, organizations must carefully look at how these other groups handle privacy and set up strict data-sharing deals.

Data Quality and Integrity

Maintaining the accuracy and integrity of big data becomes a challenge, as errors or inaccuracies in the data can lead to biased analyses and decisions.

Secondary Use of Data

Big data often involves the collection and integration of various datasets. People worry about privacy when information taken for one reason is used for something else without their knowledge or permission.

Discriminatory Algorithms

Biases and discrimination may develop when big data algorithms are used for decision-making, such as employment screening or loan approvals. These attitudes can keep people from getting fair treatment and violate their privacy rights.

 

Hypothetical Real-Life Scenario

Hypothetical Real-Life Scenario

Here’s a hypothetical scenario that will help you better understand the privacy concerns regarding big data.

Data Breach and Privacy Concerns

A major social media platform with billions of users worldwide experiences a catastrophic data breach, shaking the foundations of online privacy. Hackers breached the platform's servers and stole personal data, private messages, and browsing habits.

Leakage of Private Information

The leaked data has a lot of private information, like people's names, phone numbers, email addresses, and even cash information. The hack affects people from all walks of life, from everyday users to celebs, leaders, and other powerful people.

Escalation of Risks and Consequences

As word of the breach spreads like wildfire, worries about the privacy of big data rise, and the effects can be felt worldwide. Soon after the hack, there were stories of attackers using the information they stole to do other bad things.

Phishing attempts are on the rise, and scammers act as known organizations to get more personal information from people who don't know what's happening.

Financial Scams and Identity Theft

Hackers use the leaked financial information to get into bank accounts and credit cards without permission, so the number of financial scam cases is rising. People whose private texts were leaked have their private talks posted all over the internet, which hurts their image and makes it hard for them to get along with others.

When private pictures, videos, and talks are leaked, they invade the private lives of celebrities and other public figures. This makes people angry and makes them less likely to trust online platforms.

Global Impact and Calls for Action

As governments and regulatory groups try to figure out how big the breach is, calls from the public for stronger steps to protect personal information hit a boiling point.

The social media platform is in trouble with the law because users and privacy groups are filing cases against it. The event has started discussions worldwide about the need for stricter data privacy rules, more openness, and responsibility from tech giants.

Response and Recovery Efforts

In response to the crisis, the platform implements extensive security measures in response to the problem, including multifactor authentication, encryption, and regular audits. They put together a special team to investigate the breach, work with law enforcement agencies, and talk openly with harmed users.

Nonetheless, the damage to individuals' privacy and trust in online platforms has impacted how society perceives and approaches big data privacy concerns.

"Data breaches shake public trust to the core. To rebuild essential user confidence, radical transparency is key - communicating openly, showing genuine concern for people’s privacy rights and preventive upgrades being implemented. Simultaneously, providing access to credit monitoring, insurance safeguards if identities are compromised. This upholds duty of care in crisis. Looking inward, urgent focus mandatory on data governance gaps, outdated systems permitting breaches. Opportunity lies in learning, emerging more secure by plugging weaknesses.”

 

The Role of Cybersecurity Services in Addressing Big Data Privacy Concerns

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Organizations can benefit from partnering with cybersecurity service providers to address big data privacy concerns effectively. These providers specialize in protecting data, implementing security controls, and ensuring compliance with privacy regulations.

Organizations can leverage expertise, advanced technologies, and best practices by outsourcing cybersecurity services to protect their data.

Data Protection and Encryption

Cybersecurity providers can help organizations protect private data throughout its lifecycle by implementing strong protection systems. This includes data at rest, in motion, or being analyzed. This is done to protect privacy and data security.

Access Controls and User Authentication

Robust access controls and user authentication mechanisms help organizations limit data access to authorized personnel only, reducing the risk of unauthorized use or disclosure.

Threat Detection and Incident Response

Cybersecurity services offer proactive ways to find threats, such as advanced analytics and AI-driven techniques, so that they can quickly find and react to possible data breaches or security issues.

Compliance with Regulations

Cybersecurity providers are well-versed in privacy regulations like HIPAA, GDPR, and CCPA. They assist organizations in aligning their big data practices with these regulations, ensuring legal compliance and reducing the risk of penalties or legal consequences.

Privacy Impact Assessments (PIAs)

Cybersecurity companies can help organizations do PIAs to determine what privacy risks big data projects might pose. This helps find and put in place the right safety measures and limits.

Employee Training and Awareness

Comprehensive cybersecurity services include employee training programs to raise awareness about privacy best practices, such as data handling, depreciation, and the importance of protecting sensitive information.

Proactive Monitoring and Detection

Cybersecurity services employ advanced real-time monitoring tools and techniques to detect potential threats and anomalies, enabling proactive measures to mitigate risks before they escalate.

Future Consideration

In the future, as the volume and complexity of big data continue to expand exponentially, the consideration of privacy concerns will be of utmost importance. With the rise of linked devices, the Internet of Things (IoT), and advanced data analytics, people's personal information will likely be misused and exploited.

So, protecting data privacy will require full rules, strong security, and clear permission guidelines to ensure people have control over their data. Finding a balance between using the huge potential of big data for social good and protecting people's privacy will be the biggest challenge of the future. This will require constant improvements in both technology and regulations.

Final Thoughts

As organizations use the power of big data to drive innovation and gain a competitive edge, they must also be aware of the privacy issues that come with it. Organizations can proactively address big data privacy concerns and protect sensitive information by engaging cybersecurity services from reputable providers.

These services offer full data protection, risk assessment and reduction, experience in compliance, and the ability to respond to incidents. Investing in protection services shows a commitment to privacy, builds customer trust, and protects organizations from the potentially damaging effects of privacy breaches in the big data world.

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