1. ADAPTIVE DATA ANONYMIZATION AGAINST INFORMATION FUSION BASED PRIVACY ATTACKS ON ENTERPRISE DATA Srivatsava Ranjit Ganta, Shruthi Prabhakara, Raj Acharya Department of Computer Science and Engineering Penn State University
ABSTRACT :
Data Privacy is one of the key challenges faced by enterprises today. Enterprises manage several individual-specific sensitive information such as customer data, employee records etc on a daily basis.
Anonymization techniques (ex. k-anonymity) allow enterprises to safely release these sensitive data such that individual privacy is preserved while allowing organizations to maintain and share such valuable information.
However, current anonymization techniques are prone to attacks where-in an intruder can fuse auxiliary information with the anonymized data to infer sensitive information.
In this poster, we demonstrate a Information Fusion Based Privacy Attack on anonymized enterprise data and propose a prototype solution to address this problem.
INFORMATION FUSION BASED PRIVACY ATTACK:
Consider the possibility in which an adversary (possibly an insider) who is given (or otherwise acquires) access to anonymized release to estimate the sensitive data.
To achieve this, he uses the identifier attributes present in the release to search for additional information about the customers from other sources such as web.
Abundant individual-specific information is available on the web through homepages, blogs, personals etc. The adversary then uses his understanding of the data and fuses the anonymized release and web-based auxiliary information to estimate the sensitive data.
The goal of this research is to demonstrate such a Web-based Information Fusion Attack on enterprise data.
GOAL:
Given a sensitive private dataset P, the goal is to compute Fusion-Resilient Anonymization Pˊ from P such that:
Pˊ is resilient to Information Fusion based Privacy Attacks.
The utility U offered by Pˊmeets the release requirements.
Given a sensitive private dataset P, web-based auxiliary information Q, an information fusion system F, compute an anonymized dataset Pˊsuch that the weighed sum of adversarial estimation error and utility computed as: H = W1 * (P○P) + W2* U
is maximized, where P represents the estimate of P made by the adversary using Pˊ, Q and F. U represents the utility of the released dataset, (D1 ○ D2) represents the dissimilarity between two datasets D1 and D2 and W1 and W2 are the weights assigned for privacy protection against information fusion attacks and data utility respectively.
SOLUTION:
EXPERIMENTAL RESULTS:
Information Gain (Figure 3): Before Information fusion the dissimilarity between the original data and released data is (P○Pˊ). After Information Fusion, the adversary’s estimate P is closer to P when compared to Pˊ. The difference between P○Pˊ and P○P is the amount of Information Gain by the adversary through fusion.
Optimal Anonymization (Figure 5): We use the discernability metric defined in [1] to measure utility of a k-anonymized data set . For a value of k=12, the resulting anonymization offers maximum weighted sum of privacy protection and utility.
CONCLUSION:
This research sheds light on the shortcomings of existing anonymization schemes in the context of enterprise data.
We defined a Information Fusion based Privacy Attacks wherein an adversary uses publicly available web-based information along with the anonymized data to inflict a privacy breach.
We also formulated the problem of finding a fusion resilient data anonymization and propose one possible solution to address this problem.
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