Abstract
This article critically examines the Private Question Authentication (PQA) framework, a cognitive-based zero-knowledge authentication mechanism proposed as a countermeasure to AI- and quantum-enabled identity attacks. While the theoretical model claims near-total resistance to brute force, adversarial AI, and quantum computation, this paper evaluates such claims within the context of existing literature on post-AI security. Specifically, it explores the balance between computational infeasibility and human usability, highlighting the paradox of relying on ephemeral, unpublished knowledge that exists solely within human memory. Furthermore, the analysis considers challenges in environments lacking pre-established trust and compares PQA with biometric and behavioral authentication systems. The discussion concludes that while PQA represents a theoretically robust paradigm shift, its scalability and long-term usability remain unresolved
Introduction
The evolution of artificial intelligence (AI) and deepfake technologies has undermined many conventional identity verification systems. Passwords, one-time codes, and biometrics are increasingly vulnerable to model inference, spoofing, and quantum-powered cryptanalysis (Mirsky & Lee, 2021; Tolosana et al., 2020). Against this backdrop, Ghazouani (2025) introduced Private Question Authentication (PQA) as a zero-knowledge, human-centric mechanism based on unpublished cognitive tokens. Unlike biometrics or cryptographic keys, PQA relies on mutually agreed-upon knowledge shared exclusively between trusted parties, thus existing outside any dataset exploitable by machine learning or quantum algorithms The central claim is that PQA is “computationally infeasible to breach”, as no statistical or algorithmic approach can replicate unpublished, ontological human memory. However, such theoretical immunity must be weighed against practical challenges of usability, memory reliability, and scalability in real-world deployments
2 Theoretical Basis of PQA and Its Claimed Resistance
2.1 Ontological Privacy and Zero-Knowledge Principle
PQA operates under what Ghazouani (2025) terms Secret Non-Public Knowledge Authentication. The key premise is that knowledge absent from any digital dataset is inherently resistant to AI-driven prediction and quantum brute force. Unlike passwords or biometrics, which exist in structured spaces, PQA leverages ontological privacy idiosyncratic memories that cannot be generalized into statistical models
2.2 Immunity to AI and Quantum Attacks
Artificial Intelligence: AI systems, including GANs and LLMs, require training data. Since PQA knowledge is unpublished, there is no input-output mapping for inference. Thus, even advanced models are reduced to random guessing (Goodfellow et al., 2014)
Quantum Computing Quantum algorithms such as Shor’s or Grover’s presuppose structured mathematical spaces (Bernstein & Lange, 2017). As PQA lacks computable mappings quantum acceleration offers no advantage This leads to the strong claim that PQA is non-computable by design, rendering it theoretically unbreakable
3. Practical Challenges and Limitations
3.1 Reliance on Human Memory
While the system’s resistance stems from unpublished knowledge, this also constitutes its greatest weakness. Human memory is subject to decay, stress-induced lapses, and cognitive overload (Coventry, De Angeli, & Johnson, 2003). Forgotten or confused answers may lead to false negatives, denying access to legitimate users. Unlike passwords, PQA offers no reset function, as secrets are never stored
From a cognitive psychology perspective, mitigating the reliance on human memory in the PQA framework may be possible through the strategic design of authentication prompts that exploit the robustness of episodic and emotional memory. Empirical research suggests that emotionally salient experiences such as significant life events, interpersonal milestones, or emotionally charged encounters are encoded with greater depth and durability than neutral facts (Tulving, 2002; Kensinger & Schacter, 2008). These memories are typically stored with contextual details, including time, place, and affective tone, which strengthens retrieval pathways and makes them less susceptible to ordinary forgetting or interference. By embedding authentication questions within this domain of episodic memory, the PQA system could improve recall accuracy under stress or cognitive load, thereby reducing the likelihood of false negatives for legitimate users In addition, the unique, highly personal nature of episodic memory enhances security. Unlike semantic memory (e.g., general knowledge) or procedural memory (e.g., motor skills), episodic memory resists abstraction into patterns exploitable by adversarial AI models. For example, a question such as “What dish did we cook together for your graduation celebration?” relies not only on factual content but also on affective association and contextual embedding. This combination is idiosyncratic to the individuals involved and remains inaccessible to external datasets or inference engines Nevertheless, the incorporation of episodic cues introduces new design considerations. Overly intimate prompts may risk inadvertent disclosure in insecure environments or raise privacy concerns if users feel uncomfortable invoking sensitive memories. Furthermore, reliance on emotional memory could make authentication performance uneven, as individuals vary in the strength and resilience of their episodic recall. Therefore, a hybrid design that combines episodic prompts with structured memory aids—such as periodic rehearsal, cue-based scaffolding, or controlled rotation of questions—may balance usability and security. This approach aligns with established findings in cognitive psychology, which show that retrieval practice and contextual cueing can significantly enhance long-term retention (Roediger & Butler, 2011)
Kensinger, E. A., & Schacter, D. L. (2008). Memory and emotion. Handbook of emotions, 3, 601–617.
Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27.
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53(1), 1–25.
3.2 Scalability and Absence of Pre-Established Trust
The model presupposes pre-existing relationships between parties. In first-time interactions—such as onboarding clients or verifying strangers—PQA becomes infeasible, since no shared secret exists (Ghazouani, 2025). This limits its applicability in large-scale systems, customer authentication, or public digital services.
3.3 Vulnerability to Social Engineering and Contextual Leaks
Although unpublished, private knowledge may still be inferred through social interactions, casual disclosure, or surveillance of conversations. The framework’s strength collapses if contextual details enter the public sphere, highlighting a dependence on disciplined secrecy practices
4. Usability versus Security : The Trade-off
The usability-security trade-off has long been central to authentication research (Garfinkel & Miller, 2015). PQA epitomizes this dilemma
Security Strength: Derived from unpredictability and non-repetition
Usability Weakness Users must recall ephemeral secrets without assistance, potentially increasing friction, fatigue, and adoption resistance This trade-off suggests that PQA may be suited primarily for high-security, low-frequency contexts (e.g., defense operations, scientific authorship verification) rather than daily consumer authentication
5 Comparative Analysis with Biometric and Behavioral Authentication
5.1 Biometrics
Biometrics such as fingerprints, iris scans, and facial recognition are widely deployed due to usability. However, they face rising risks from deepfake forgery and irrevocability upon compromise (Garcia-Penas et al., 2022). Unlike PQA, biometrics can be collected without user effort, but once stolen, they cannot be replaced.
5.2 Behavioral Authentication
Behavioral systems analyze keystrokes, gait, or speech patterns. These are dynamic but still statistically inferable. AI adversaries can approximate such patterns, especially if training data is abundant.
5.3 PQA in Comparison
Feature Biometrics Behavioral PQA
Resistance to AI / Deepfakes Weak Moderate Very High
Reset Capability None Limited None
Usability / High / Medium / Low
Dependence on Memory / None None / High
Scalability / High / Medium / Low
PQA demonstrates superior theoretical resistance but is hindered by usability and deployment constraints.
6. Toward Hybrid and Alternative Models
Given PQA’s practical limitations, hybrid models may address its shortcomings
Multi-Layered Authentication Combining PQA with biometrics or hardware tokens to reduce memory burden
Contextual Key Generation Using dynamic event-based knowledge (e.g., real-time cues) to reduce reliance on long-term memory
Trusted Intermediaries Employing secure devices to generate temporary “knowledge surrogates” in contexts without prior relationships
Such adaptations could preserve PQA’s theoretical robustness while mitigating user-related vulnerabilities
Conclusion
The Private Question Authentication framework represents a novel departure from data-driven verification systems, introducing an ontological dimension of security that resists AI and quantum computation. Its theoretical claim of being “computationally infeasible to breach” is well-founded under clean assumptions. However, the framework faces significant practical obstacles: reliance on fallible human memory, scalability barriers in non-trust contexts, and susceptibility to inadvertent disclosure When compared with biometrics and behavioral systems, PQA offers unmatched resilience against emerging computational threats but lacks the usability and scalability necessary for mass adoption. Future research should focus on empirical usability studies, hybrid authentication models, and mechanisms for establishing shared knowledge in trustless environments PQA thus stands not as a universal replacement but as a specialized, high-assurance tool for contexts where computational immunity outweighs convenience
References
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Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion.