Homomorphic Encryption has transitioned from being a mathematical curiosity to a linchpin in fortifying machine learning workflows against data vulnerabilities. Its complex nature notwithstanding, the unparalleled privacy and security benefits it offers are compelling enough to warrant its growing ubiquity. As machine learning integrates increasingly with sensitive sectors like healthcare, finance, and national security, the imperative for employing encryption techniques that are both potent and efficient becomes inescapable.
Differential Privacy is a privacy paradigm that aims to reconcile the conflicting needs of data utility and individual privacy. Rooted in the mathematical theories of privacy and cryptography, Differential Privacy offers quantifiable privacy guarantees and has garnered substantial attention for its capability to provide statistical insights from data without compromising the privacy of individual entries. This robust mathematical framework incorporates Laplace noise or Gaussian noise algorithms to achieve this delicate balance.
Model stealing, also known as model extraction, is the practice of reverse engineering a machine learning model owned by a third party without explicit authorization. Attackers don't need direct access to the model's parameters or training data to accomplish this. Instead, they often interact with the model via its API or any public interface, making queries (i.e., sending input data) and receiving predictions (i.e., output data). By systematically making numerous queries and meticulously studying the outputs, attackers can build a new model that closely approximates the target model's behavior.
Whether AI and the technologies it enables will reach their full potential depends on the workforce that will work alongside them. Yet the skills that that workforce needs to do this are in short supply. Rather than debating what to do about massive job losses from AI, discussion should focus on how best to prepare workers' skills for the types of jobs that they will need to fill.
A shifting job picture
A 2017 McKinsey’s report says that approximately half of all activities done by the current workforce could be automated. They point out, however, that this does not point to...
In recent years, the rise of artificial intelligence (AI) has revolutionized many sectors, bringing about significant advancements in various fields. However, one area where AI has presented a dual-edged sword is in information operations, specifically in the propagation of disinformation. The advent of generative AI, particularly with sophisticated models capable of creating highly realistic text, images, audio, and video, has exponentially increased the risk of deepfakes and other forms of disinformation.
Backdoor attacks in the context of Machine Learning (ML) refer to the deliberate manipulation of a model's training data or its algorithmic logic to implant a hidden vulnerability, often referred to as a "trigger." Unlike typical vulnerabilities that are discovered post-deployment, backdoor attacks are often premeditated and planted during the model's development phase. Once deployed, the compromised ML model appears to function normally for standard inputs. However, when the model encounters a specific input pattern corresponding to the embedded trigger, it produces an output that is intentionally skewed or altered, thereby fulfilling the attacker's agenda.
Recent events have confirmed that the cyber realm can be used to disrupt democracies as surely as it can destabilize dictatorships. Weaponization of information and malicious dissemination through social media pushes citizens into polarized echo chambers and pull at the social fabric of a country. Present technologies enhanced by current and upcoming Artificial Intelligence (AI) capabilities, could greatly exacerbate disinformation and other cyber threats to democracy.
Label-flipping attacks refer to a class of adversarial attacks that specifically target the labeled data used to train supervised machine learning models. In a typical label-flipping attack, the attacker changes the labels associated with the training data points, essentially turning "cats" into "dogs" or benign network packets into malicious ones, thereby aiming to train the model on incorrect or misleading associations. Unlike traditional adversarial attacks that often focus on manipulating the input features or creating adversarial samples to deceive an already trained model, label-flipping attacks strike at the root of the learning process itself, compromising the integrity of the training data.
Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities that pop up in complex AI systems, not because they were explicitly trained to exhibit them, but due to the intricate interplay of the system's complexity, the sheer volume of data it sifts through, and its interactions with other systems or variables. It's like giving a child a toy and watching them use it to build a skyscrapper. While scientists hoped that scaling up AI models would enhance their performance on familiar tasks, they were taken aback when these models started acing a number of unfamiliar tasks.
Model Evasion in the context of machine learning for cybersecurity refers to the tactical manipulation of input data, algorithmic processes, or outputs to mislead or subvert the intended operations of a machine learning model. In mathematical terms, evasion can be considered an optimization problem, where the objective is to minimize or maximize a certain loss function without altering the essential characteristics of the input data. This could involve modifying the input data x such that f(x) does not equal the true label y, where f is the classifier and x is the input vector.
Ask people on the street how much AI uses today affect their lives, and most would probably answer that it doesn’t affect them right now. Some might say that it’s pure science fiction. Others might say that it may affect our future but isn’t used in our world today. Some might correctly identify a few ways it’s used in modern technology, such as voice-powered personal assistants like Siri, Alexa and Cortana. But most would be surprised to find out how widely it is already woven into the fabric of daily life.
Data poisoning is a targeted form of attack wherein an adversary deliberately manipulates the training data to compromise the efficacy of machine learning models. The training phase of a machine learning model is particularly vulnerable to this type of attack because most algorithms are designed to fit their parameters as closely as possible to the training data. An attacker with sufficient knowledge of the dataset and model architecture can introduce 'poisoned' data points into the training set, affecting the model's parameter tuning. This leads to alterations in the model's future performance that align with the attacker’s objectives, which could range from making incorrect predictions and misclassifications to more sophisticated outcomes like data leakage or revealing sensitive information.
Trust comes through understanding. As AI models grow in complexity, they often resemble a "black box," where their decision-making processes become increasingly opaque. This lack of transparency can be a roadblock, especially when we need to trust and understand these decisions. Explainable AI (XAI) is the approach that aims to make AI's decisions more transparent, interpretable, and understandable. As the demand for transparency in AI systems intensifies, a number of frameworks have emerged to bridge the gap between machine complexity and human interpretability. Some of the leading Explainable AI Frameworks include:
Cybersecurity strategies need to change in order to address the new issues that Machine Learning (ML) and Artificial Intelligence (AI) bring into the equation. Although those issues have not yet reached crisis stage, signs are clear that they will need to be addressed – and soon – if cyberattackers are to be prevented from obtaining a decided advantage in the continuing arms race between hackers and those who keep organizations’ systems secure.
Data masking, also known as data obfuscation or data anonymization, serves as a crucial technique for ensuring data confidentiality and integrity, particularly in non-production environments like development, testing, and analytics. It operates by replacing actual sensitive data with a sanitized version, rendering the data ineffective for malicious exploitation while retaining its functional utility for testing or analysis.