By: Dadapeer Agraharam Shaik, Department of Computer Science and Technology, Student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu,517325, Andhra Pradesh.
Abstract:
This is because the element of Artificial Intelligence (AI) is being incorporated prominently in enhancing the data privacy and security aspect. The author in this article seems to try and explain how AI is applied in the guarding of information, managing of risks as well as legal matters. When criminals pose threats, their intent clearly denotes that with criminals in positions of power, an organization is at risk of being tightened and overtaken by them; thus, with the use of artificial intelligence, such threats can be identified, responses made, as well as security measures initiated to enhance the organization’s security. The coordination of using AI with data privacy and protection fortifies the line between cyber threats and reliable solutions without jeopardizing credibility and dependable digital surroundings.
Keywords: Data Protection/Data Privacy, Artificial Intelligence, Cybersecurity.
1.Introduction
In this level of technology and networking sharing of data has become an issue that needs protection in any a community, business, and government. Therefore, it is highly important to enhance the protection of the corresponding data as the amounts of data increase and the existing threats and challenges grow continuously. One of them that has recently been actively discussed in this regard is Artificial Intelligence (AI), and it has new approaches to further enhance the level of protection for the data. All in all, AI complements the best approaches for threat, compliance, as well as protection efforts to boost the levels of data privacy and security. In light of the content of this article, this paper shall identify how numerous forms of AI technologies assist in the process of data protection and shall thereafter analyse the prospects as well as the challenges of AI data privacy and protection prospects.
2.Privacy and Security of Big Data in AI
In Privacy and Security of Big Data in AI There are many methods and some of the methods are mentioned below.
- Data Breach
Data breach is one of the typical forms of privacy violation, which implied unauthorized access to personal data. AI, by enhancing insights from big data, introduces new vulnerabilities to data and privacy breaches at various phases: Three stages in deploying a machine learning model are training, model, and inference. Initial instances are re-identification attacks, for instance using publicly available electoral registers, match with the medical records or with the help of mobile phone metadata which shows the location of the individuals. Thus, although measures have been made in an attempt hide information, AI’s advancement has brought new ways of infringements in privacy[1].
- Bias in Data
Automated decisions can introduce and maintain bias in the credit approvals, employment decision, and more using the algorithm and databases. Such bias in AI come from data that are used in training; sample bias, algorithm bias and prejudicial bias will affect decision-making attributes like gender, race, and age. For instance, facial recognition on gender from AI has issues with high error rates when comparing darker skin females to light skin males. Other systems, for example, COMPAS used to decide on criminal sentencing also display racism and marks black offenders as higher risk than the whites[2].
- Data Poisoning
When learning, data poisoning is a process through which an adversary introduces malicious inputs in order to make a model or systems give incorrect outcomes deliberately. This attack destroys the availability of a system by changing the classifier boundary or the system by setting up back doors. As little as one contaminated instance can bring an AI model to its knees as evidenced by instances such as stop sign identification fail and abusive responses from chatbots. Data poisoning influence numerous domains such as sentiment analysis and malware detection.
- Model Extraction
Adversarial machine learning attack on this category pertains to the theft of the learned model of an ML model by approximation of the records employed in the learning process or by replicating models through input-output pattern recognition or interrogation. This results in very high cases of privacy violation, especially in data that require tight security such as the medical records. This attack can be potentiated as many of the ML techniques like Logistic Regression and Neural networks are prone to it and the defense measures are inadequate to safeguard the privacy of data.
- Evasion
Evasion attacks are a type of evasion that dodges being detected by misleading the systems to wrongly classify them, usually through adversarial examples which are slightly modifying the input. Unlike data poisoning where there is a change in classification boundaries, evasion attacks change inputs to lead misclassification when the AI workflow gets to its application phase. In computer vision, such evasion attacks can be very dangerous since they result in harmful misclassifications for instance interpreting stop signs wrongly in self-driven automobiles.[1]
3.Data Protection and Privacy
One of the main limitations of machine learning and deep learning approaches is their requirement for large datasets for development and testing—datasets that are typically much larger than those collected in most prospective clinical trials. Compared to other medical specialties, ophthalmology has benefited from the widespread availability of large, well-curated imaging datasets and is often seen as being at the forefront of AI-enabled healthcare. Although the availability of anonymized datasets has driven technological advancement, it also represents a significant risk. The principle of beneficence requires that healthcare professionals “do no harm,” yet breaches of patient privacy can cause major harm and have unintended consequences, potentially impacting employment or insurance coverage and allowing hackers to obtain Social Security numbers and personal financial information[3].
Removing all potentially identifiable information from large datasets can be a daunting task. Even with the most rigorous efforts, there will always remain at least a theoretical risk of re-identification. This issue is not unique to ophthalmology, as it is now conceivable to apply facial recognition software to three-dimensional reconstructions of computed tomography of the head. In addition, features from the periocular region have been used to identify the age of patients using machine learning algorithms. Gender, age, and cardiovascular risk factors have been identified from fundus photographs. Even for datasets not involving medical images, and even without the use of advanced or future technologies, it may be possible to identify individuals by linkage with other datasets, particularly as patient information generally accumulates over time.[4]
4.The Impact of AI Systems on Key Elements of Privacy
With these remarks about the general nature of privacy in place, it is now possible to use these understandings to provide perspective on the impact of artificial intelligence on privacy concerns. We will focus on three aspects of privacy that we have noted: the notion of epistemic privilege, the element of control or consent, and the element of the feedback loop, with corresponding implications for the distinction between security interests and interests in privacy per se.
- Epistemic Privilege
Epistemic privilege applies to the notion that people are more informed about themselves than others and therefore, are in the best position to decide what to divulge and what not to divulge concerning themselves. However, AI and a system of mass surveillance do not fit into the described background. Governments and private companies through automation collect huge data from almost all individuals who are using digital devices to some extent unknowingly. This data collection is continuous and centralized thus making it almost impossible for these individuals to retain the discretion on such data. This problem is extended when both ubiquitous computing and ambient intelligence technologies are incorporated due to the latter’s constant and hidden monitoring and data analysis, thus possessing more information about certain people than the people themselves.
- Consent and Control
As it is usually in face-to-face communications, one can regulate the information he or she provides to the other party and has control over what is being revealed through consent. However, the AI-infused data gathering systems are embedded with data subjects in most contexts and gather and profile them unobtrusively. This is a departure from normal encounters in public domains where people can easily control how they are viewed or observed, and where one’s privacy can be protected. Automated processing of data also takes place without the knowledge of individuals and also creating professional profiles of the subjects. Such a shift is fatal in so far as it erodes the capacity of people to govern their identity and privacy satisfactorily[5].
- Feedback Loop
AI systems overlay a new layer into the nature of feedback concerning privacy. Consequently, in ordinary interactions, feedbacks given to individuals are determined by the information people give out, so as to fit into the new environment and adjust to what they intend to reveal. However, AI systems create profiles and conclusions from large amounts of data and the subjects of such data may not be aware of it. Such a process automates the assessment and tracking of people, Their data thus becomes a recurring cycle of profiling, which becomes hard to manage the privacy of information.[6]
CONCLUSION:
Therefore, in the discourse of predicting the future of data privacy and protection, AI has a central role to play. AI can improve the security mechanisms, detect those risks which maybe could lead to a breach, and meet new standards and requirements more efficiently. But it is also adding new concerns into the data privacy frameworks such as the issues of transparent and ethical Artificial Intelligence. Thus, given that technology is rapidly evolving, having a proper mix of artificial intelligence with corporate governance and other strong controls, as well as adequate legal frameworks, will play an important role in providing protection to personal information and establishing confidence in the use of such tools. The opportunity to adopt such innovations and regulate the connected risks will be significantly vital for shaping a more protected and receptive context for the digital citizens.
Reference:
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Cite As
Shaik D.A. (2024) The Role of AI in Data Privacy and Protection, Insights2Techinfo, pp.1