Statistical Database Security: Unique Attack Risks

TL;DR

Statistical databases (like those used for reporting or analytics) have specific weaknesses attackers can exploit, beyond typical database security concerns. These relate to data reconstruction from aggregates, inference attacks, and the potential for revealing sensitive information even without directly accessing raw data. This guide covers common attack vectors and practical steps to protect your systems.

Understanding the Risks

Traditional database security focuses on confidentiality (keeping data secret), integrity (ensuring accuracy) and availability. Statistical databases add complexity because their purpose is often *to* share information, but in a controlled way. This means attackers don’t always need to steal entire tables; they can infer details from the summaries.

Attack Vectors & Solutions

1. Attribute Disclosure: Attackers use published statistics to learn about individual attributes.
   • Risk: Simple counts or averages can reveal information. For example, knowing the average salary in a small department might pinpoint an individual’s income.
   • Solution: Data Suppression – Don’t publish statistics for very small groups (e.g., fewer than 5 individuals). Set a minimum threshold.
2. Composition Attacks: Combining multiple published statistics to reveal more information.
   • Risk: Aggregates that seem harmless individually can be combined to deduce sensitive data. For example, knowing the total number of patients with a rare disease *and* the total number of patients in a specific age group could narrow down individual identities.
   • Solution: Differential Privacy – Add random noise to the published statistics. This makes it harder to pinpoint exact values but preserves overall trends. Tools like Google’s Differential Privacy library can help.

     # Example (Python) - Simplified concept, real implementations are more complex
     import numpy as np
     
     def add_noise(data, epsilon):
         noise = np.random.laplace(0, 1/epsilon, len(data))
         return data + noise
     

3. Re-identification Attacks: Linking published statistics back to individuals.
   • Risk: If the database contains quasi-identifiers (attributes that aren’t unique on their own but become so when combined, like postcode and date of birth), attackers can match these with external datasets.
   • Solution: k-Anonymity – Ensure each record in the underlying data is indistinguishable from at least k-1 other records based on quasi-identifiers. This requires careful data masking or generalisation.

     # Example (Conceptual) - Generalising postcodes
     Postcode = 'NW1 0AA'
     Generalised_Postcode = 'NW1 *AA' # Masking the last two characters
     

4. Inference Attacks: Deducing information about individuals not directly present in the database.
   • Risk: Attackers can infer characteristics of a population based on statistics. For example, if you publish data about hospital admissions for a specific condition, attackers might infer the prevalence of that condition in certain areas.
   • Solution: Aggregation Control – Carefully control how data is aggregated and published. Avoid publishing combinations of attributes that could lead to inference attacks. Consider using privacy-preserving machine learning techniques.
5. SQL Injection (Still Relevant!): Although statistical databases often use different query languages, they are still vulnerable to injection if not properly secured.
   • Risk: Attackers can manipulate queries to access unintended data or modify the database.
   • Solution: Input Validation & Parameterised Queries – Always validate user input and use parameterised queries (prepared statements) to prevent SQL injection attacks. This is standard database security practice.

     # Example (PHP - using PDO)
     $stmt = $pdo->prepare('SELECT * FROM users WHERE username = ?');
     $stmt->execute([$username]);
     

Practical Steps

1. Data Minimisation: Only collect and store the data you absolutely need.
2. Access Control: Implement strict access controls to limit who can view or modify the database.
3. Regular Audits: Regularly audit your database security measures and published statistics for potential vulnerabilities.
4. Privacy Impact Assessments (PIAs): Conduct PIAs before publishing any new statistical data.
5. Stay Updated: Keep your database software and related tools up to date with the latest security patches.