GDPR-Compliant AI: Privacy-Preserving Machine Learning Techniques

Artificial Intelligence, and machine learning more generally, are revolutionising many different sectors around the globe, but one sector is undergoing a quiet transformation: the tourism sector, allowing organisations to analyse vast amounts of data for insights and improved decision-making. However, with great data comes great responsibility: ensuring compliance with privacy regulations like the EU’s General Data Protection Regulation (GDPR) and Australia’s updated Privacy Act 2024. Personal data in tourism is not limited to obvious identifiers (names, dates of birth, etc.), but includes less obvious information such as location traces, travel itineraries, and aggregated behavioural data that can still identify or profile individuals. Studies have shown that even anonymised or aggregated mobility datasets can be re-identified by linking unique travel patterns. In other words, information like where tourists go, how often, or in what combinations can inadvertently reveal personal identities if misused. This raises concerns for government tourism bodies and businesses that leverage sophisticated AI on big data: they must employ equally sophisticated privacy-preserving techniques and compliance measures to protect personal information and maintain public trust.

Senior leaders in tourism and academia alike are recognising that privacy is not just a compliance checkbox but a strategic priority. This paper explores privacy-preserving machine learning (ML) techniques, including federated learning, differential privacy, and homomorphic encryption, as key enablers of GDPR compliance in AI-driven tourism analytics. We will examine how these technical approaches help organisations use data responsibly, discuss current regulatory challenges and best practices (globally and in Australia’s context), and highlight real-world case studies demonstrating privacy-preserving AI in tourism. The tone blends business pragmatism with academic rigour, aiming to inform decision-makers and researchers on building privacy by design into AI initiatives in the tourism industry.

Redefining PII in Tourism: Beyond Names and IDs

In the tourism domain, personally identifiable information (PII) extends far beyond basic contact details. GDPR defines personal data broadly as any information relating to an identified or identifiable individual, this explicitly includes online identifiers and location data when they can pinpoint a person. For example, the GPS trails left by a traveller’s mobile phone, check-in timestamps, or even aggregated foot-traffic heatmaps could fall under personal data if they enable someone to discern an individual’s movements or habits. A seemingly anonymous dataset of tourists’ movements can become identifying when combined with other data; unique travel patterns often serve as fingerprints that make individuals stand out.

Privacy risks in tourism data: Consider that a tourism board collects mobile location data to understand visitor flows through a city. Even without names, if one person’s path is unique (e.g. a VIP tourist attending private events), an attacker could recognise that pattern. In one study on mobile phone data, researchers found that many users can be easily re-identified based on unique mobility patterns despite data being aggregated. Likewise, purchasing records or hotel stays, if combined, might reveal a traveller’s preferences, health status (visits to clinics or pharmacies), or religious affiliation (visits to places of worship). These examples identify that PII in tourism encompasses any data that can single out or profile a person, intentionally or not.

Case in point: Lithuania Travel’s Mobile Data Project (2022) provides a positive example of using big data for tourism while respecting privacy. The national tourism agency leveraged anonymised mobile phone data from a telecom operator to generate rich insights into inbound visitor travel patterns. They created public dashboards showing aggregated indicators, like heatmaps of tourist movements and stay durations, but the data was fully anonymised and depersonalised before analysis. By focusing on statistical indicators rather than individual trajectories, the project gained granular tourism insights (e.g. popular routes, peak visit times) without exposing any one person’s identity. This illustrates how tourism bodies can innovate with data analytics yet uphold GDPR’s principle that personal data be either anonymised or securely protected. The takeaway for leaders is clear: even high-level or aggregated data can carry privacy risks if improperly handled, so robust anonymisation and privacy-preserving methods are crucial from the outset.

Privacy-Preserving Machine Learning Techniques

To reconcile the demand for data-driven innovation with strict privacy requirements, organisations are turning to technical solutions collectively known as privacy-preserving machine learning. These approaches allow AI models to learn from data or share insights without exposing sensitive personal information. The three foundational techniques are: federated learning, differential privacy, and homomorphic encryption. Each tackles privacy from a different angle, and often they are used in combination for maximum protection. Below, we outline each technique and its relevance to GDPR compliance:

• Federated Learning (FL): Federated learning enables collaborative model training without centralising the raw data. Instead of pooling all tourism data in one place (which heightens breach risk and regulatory complexity), a central server sends a model to where the data resides (e.g. local servers at regional tourism offices or even users’ devices). The model is trained locally on each dataset, and only anonymous model updates (gradients) are sent back to be aggregated into a global model. No personal data leaves the premises. This setup inherently supports GDPR’s data minimisation and purpose limitation principles, data is kept only where necessary and not copied around needlessly. FL can significantly reduce the risk of data leakage because there is no single trove of all personal data to target. It also eases compliance with cross-border data transfer rules, since personal information stays within its source jurisdiction. For example, Google’s Gboard keyboard uses FL so that user typing data never leaves user devices; only learned predictions are centralised, protecting individual privacy. In the tourism context, federated learning could allow hotels, airlines, and tourism boards to train a shared AI model on travel trends without ever exchanging their customer databases. Such collaborative models could predict tourism demand or personalise offers while keeping each organisation’s guest data siloed and private. In fact, a recent study on airline industry upgrades demonstrated that federated learning across multiple data silos improved the prediction accuracy of customers’ willingness-to-pay for upgrades, all while preserving customer privacy. FL thus presents a win-win: better AI insights through collective learning, achieved in a GDPR-compliant manner by never pooling sensitive data centrally.
• Differential Privacy (DP): Differential privacy is a mathematical technique that protects individuals in a dataset by adding carefully calibrated noise to the outputs of data analysis. In essence, it allows organisations to share aggregate information or train models such that one cannot tell if any single person’s data was included or not. For instance, a tourism board could publish statistics like “average length of stay” or run machine learning algorithms on tourist spending data with a DP mechanism in place. The results would be accurate for overall trends, but blurred enough that no specific individual’s pattern can be isolated. Under GDPR, anonymised data (which cannot be tied back to an individual) is not subject to personal data regulations. Regulators have noted that if data is sufficiently anonymised via techniques like differential privacy, it can fall outside GDPR’s scope. The UK’s Information Commissioner’s Office (ICO) explicitly recognises that differentially private outputs, when used appropriately, count as anonymised and thereby reduce data protection risks. This means deploying DP can help tourism organisations release useful data (e.g. an open dataset of tourist movements or a machine learning API for travel recommendations) without risking personal data leaks, since any one individual’s influence on the output is masked. Differential privacy has already been adopted in practice by companies like Apple (to collect usage statistics from iPhones while obscuring user identities) and by government agencies like the U.S. Census Bureau (to publish aggregate census data with privacy guarantees). For tourism applications, DP could enable sharing of insights between agencies or with the public, such as footfall counts in destinations or popular travel itineraries, with mathematical assurances that no visitor’s privacy is compromised.
• Homomorphic Encryption (HE): Homomorphic encryption takes a different approach by allowing computations on data while it remains encrypted. Traditionally, to analyse or model data, one must decrypt it at some point, creating a vulnerability where a malicious actor or even an insider could access the plaintext information. Homomorphic encryption bypasses this by using advanced cryptography: data stays encrypted throughout processing, and the results come out encrypted, only to be decrypted by the data owner. In practical terms, a tourism organisation could encrypt its customer dataset and send it to a cloud AI service for analysis; the cloud can perform computations (like training a model or running queries) without ever “seeing” the actual personal data. The output, still encrypted, is returned, and only the tourism body can decrypt the final result. This technique is powerful for compliance because even if a cloud server is breached, the data would be unintelligible. It also helps in multi-party analytics: for example, several hotels could contribute encrypted guest data to jointly compute a pattern (say, detecting overlapping visitors or fraudulent bookings) and none of them learns anything beyond the final encrypted outcome. While fully homomorphic encryption is computationally heavy and still maturing, it has made strides in recent years. Already, there are applications in healthcare and finance using HE to allow sensitive data analysis by third parties without privacy loss. In the tourism sector, one could envision government analytics platforms using HE so that personal data from airlines, immigration, hotels, etc., is never exposed, even as combined models are being computed. Such an approach would satisfy GDPR’s “security of processing” requirement (Article 32) by rendering data unintelligible to any unauthorised party, and it aligns with Australian Privacy Principle 11’s mandate for technical measures to secure personal information.

By leveraging these privacy-preserving ML techniques, organisations can embed privacy into the design of AI systems, rather than treating it as an afterthought. Importantly, these methods are not mutually exclusive, they are often combined for defence-in-depth. For example, in federated learning deployments, researchers add differential privacy on the model updates to prevent reconstruction of any participant’s data from the shared model. They may also use secure hardware enclaves or partial homomorphic encryption to further protect data in transit. The end goal is a scenario where even if parties don’t fully trust each other, they can still collaborate through AI without exposing raw data. This paradigm shift enables powerful cross-organisation analytics (critical in tourism, which often involves multiple stakeholders) while strictly upholding individuals’ privacy rights.

Case Studies: Privacy-Preserving AI in Practice

To illustrate how privacy-preserving techniques and compliance considerations come together, we present two brief case studies relevant to tourism and travel:

• Tourism Analytics with Mobile Data, Lithuania: As mentioned earlier, Lithuania’s national tourism agency launched a Mobile Data Project that harnessed telecom data to understand tourist behaviour. The project created dashboards of inbound tourists’ movements, updated three times a year, covering various geographic levels. Key to its success was privacy by design: all data from the mobile network was anonymised and aggregated before analysis. The resulting indicators, such as counts of visitors by region, average durations of stay, and movement heatmaps, were depersonalised patterns rather than individual tracks. By collaborating with telecom and data analytics companies under strict privacy safeguards, the tourism board gained much richer insights than traditional surveys, yet remained compliant with privacy laws. No personal traveller or their exact trail can be extracted from the dashboards. This case demonstrates that government-run tourism bodies can employ sophisticated big data and AI tools without sacrificing privacy, so long as they integrate techniques like anonymisation, aggregation, and privacy audits into the project from the start. It also shows a compliance best practice: perform a Data Protection Impact Assessment (DPIA) for such initiatives to ensure all risks are mitigated (GDPR in fact mandates DPIAs for high-risk data projects). Lithuania’s approach can be a model for other tourism boards, proving that privacy-preserving analytics can drive policy and business decisions, for example, informing infrastructure planning or marketing strategies, in a way that respects individual travellers’ rights.
• Federated Learning for Airlines, Upgrade Offers: In the airline industry (a sector adjacent to tourism), privacy-preserving AI has been applied to optimise customer offers. A 2022 academic study tackled the problem of upgrading passengers (e.g. to premium class) and predicting their willingness to pay without aggregating all customer data onto one server. Airlines and their partner businesses often hold separate pieces of a traveller’s profile (flight history, hotel stays, car rentals, etc.) and combining these could improve models but typically would entail heavy data sharing and privacy risk. The study introduced a federated learning approach to break these “data silos” while protecting privacy. Using a case study of an airline, the researchers showed that multiple parties (the airline, partners) could jointly train a machine learning model on customer data without any party exposing its raw data to the others. The federated model achieved higher accuracy in predicting which customers would accept upgrade offers, compared to models trained on siloed data, thus boosting business outcomes while preserving customer privacy. They also layered encryption methods to ensure that even the intermediate model updates were secure. This case underlines a broader lesson: complex AI use cases in tourism/travel, from dynamic pricing to personalised recommendations, to fraud detection, can be pursued in a privacy-compliant way by using federated and cryptographic techniques. For senior leaders, it exemplifies that privacy enhancements are not just about avoiding fines; they can also unlock data collaboration that was previously impossible due to privacy concerns. In other words, investing in privacy-preserving ML enables innovation that respects customers’ data rights, building trust and competitive advantage.

Regulatory Challenges and Best Practices

Implementing privacy-preserving AI in tourism comes with challenges, especially as regulations continue to evolve. Globally, GDPR remains the gold standard framework, and many jurisdictions have modelled their laws on its principles of consent, transparency, data minimisation, and accountability. The GDPR (and its UK/Australian counterparts) pose several compliance challenges for AI projects:

• Transparency in Automated Decisions: GDPR mandates that if automated decisions (especially those with legal or significant effects on individuals) are made, individuals have the right to be informed and often to object or seek human intervention. Australia’s Privacy Act 2024 amendments go further, introducing requirements for transparency in automated decision-making, organisations engaging in AI-driven decisions using personal data must update their privacy policies to clearly inform individuals about such decisions. This means a tourism department using an AI algorithm to screen visa applications or travel grants automatically would need to disclose this and provide meaningful information about how the decision is made. The challenge is explaining complex AI models in simple terms and integrating disclosure mechanisms into AI systems. Best practice is to conduct algorithmic impact assessments in tandem with DPIAs, ensure explanations or notices are provided to users, and retain a human-in-the-loop where feasible for critical decisions. Senior leaders should promote a culture of algorithmic transparency, not only to comply with laws but to maintain public trust when using AI.
• Data Minimisation vs. Big Data: There is an inherent tension between AI’s hunger for large, rich datasets and the GDPR’s requirement to collect and process only the minimum data necessary for a stated purpose. Privacy-preserving techniques help resolve this tension by allowing useful insights without hoarding raw data. For example, federated learning aligns well with data minimisation, since data stays distributed and only minimal information (model parameters) is shared. Organisations should embrace architectures that avoid creating central repositories of personal data if not needed. Additionally, anonymisation and differential privacy allow them to extract value from data in aggregate form, satisfying business needs while technically adhering to minimisation (as truly anonymised data is not regulated). The best practice here is to apply Privacy by Design principles: build systems to use less personal data or use it in a privacy-safe form. This could involve techniques like data pseudonymization (replacing identifying fields with codes), early aggregation of data before analysis, and routine deletion of data that is no longer necessary. Embedding such practices in business-as-usual operations, e.g. through standard operating procedures and automated privacy controls, ensures ongoing compliance and reduces the risk of over-collection.
• Cross-Border and Multi-Party Data Sharing: Tourism is a global industry, data often flows between airlines, hotels, booking platforms, and government agencies across different countries. GDPR restricts transfers of EU residents’ data to countries without “adequate” privacy protections, requiring mechanisms like standard contractual clauses or adequacy decisions. Australia’s latest reforms introduce a similar concept: a “whitelist” of approved jurisdictions deemed to have equivalent privacy safeguards, simplifying overseas data transfers. Still, navigating multiple regulatory regimes can be complex. Privacy-preserving ML can reduce the need for raw data transfers (e.g. models travel, not the data), thus mitigating cross-border issues. When data sharing is needed, best practices include ensuring all parties in the data chain are GDPR-compliant, using contracts to extend privacy obligations to partners, and leveraging technical safeguards (encryption, access controls) during transfer. For instance, a destination marketing organisation sharing visitor analytics with an overseas research partner might employ homomorphic encryption or differential privacy so that the partner never sees personal data. This approach can satisfy regulators that even if data leaves the origin country, it remains protected to GDPR standards.
• Organisational Preparedness: One of the less technical but equally crucial challenges is building an organisational framework to support these privacy measures. Laws like GDPR and the Australian Privacy Principles require not just technical compliance but demonstrable accountability, organisations should have policies, training, and governance in place. The 2024 Australian reforms highlight that security of personal data is not purely an IT issue; it demands organisational measures like continuous staff training and robust policies. For tourism bodies adopting AI, this means investing in employee awareness (e.g. training data scientists on privacy techniques and legal requirements), appointing privacy officers or Data Protection Officers to oversee compliance, and instituting clear data handling protocols. Best practices for BAU (business-as-usual) include scheduling regular privacy audits, updating privacy notices when new AI features are introduced, and preparing incident response plans for data breaches. It’s also wise to simulate worst-case scenarios (what if an AI system accidentally memorises personal data?) and have mitigation steps ready, such as the ability to retrain models with privacy filters or shut off certain data flows quickly. Regulators are increasingly expecting proactive steps, as one Australian Privacy Commissioner put it, privacy compliance needs a roadmap and immediate action, especially as enforcement is ramping up. Leadership should therefore foster a culture where privacy-preserving innovation is celebrated and compliance is seen as everyone’s responsibility, not an obstacle.

Global Trends and the Road Ahead

Global privacy regulation is trending toward stronger protections, and AI is very much in the spotlight. GDPR set the baseline, and now regulations are converging. Australia’s 2024 Privacy Act amendments bring its principles closer to GDPR (introducing rights like a potential right to erasure, and more apparent controller/processor distinctions in the next reform tranche). We see a push for consistency in how AI systems handle personal data worldwide, which is good news for multinational tourism companies, aligning with GDPR principles can serve as a solid foundation for compliance in multiple markets. Additionally, sector-specific guidelines (such as privacy principles for mobility data or upcoming AI regulations in the EU) will further shape how AI is deployed in tourism.

Senior leaders and academics can collaborate to ensure that ethical, privacy-preserving AI becomes the norm in tourism. This involves continued research into improving privacy-tech performance (for example, making homomorphic encryption faster, or federated learning more robust against faults and attacks) and sharing best practices through industry forums. It’s also crucial to monitor how privacy-preserving methods impact model accuracy and business outcomes, the goal is to strike the right balance where privacy and performance co-exist optimally. The case studies indicate that, often, smart privacy design can actually enhance collaboration and insight, not diminish it.

In conclusion, GDPR compliance in AI is achievable and even advantageous when organisations leverage privacy-preserving machine learning techniques. Tourism organisations that adopt federated learning, differential privacy, and encryption not only shield themselves from regulatory penalties but also build trust with travellers who are increasingly privacy-conscious. By treating seemingly innocuous data (like aggregated travel logs) with the same care as obvious PII, and by embedding privacy at every layer of AI projects (from data collection to algorithm design and deployment), the tourism industry can harness AI’s power responsibly. As privacy laws tighten and technology advances, those who proactively integrate these measures will be best positioned to innovate confidently, knowing that personal data, the lifeblood of AI, is handled with due respect and protection.