Privacy-Preserving Of Data Published By Non-Interactive Private Mechanisms
In the problem of privacy-preserving collaborative data publishing (PPCDP), a central data publisher is responsible for aggregating sensitive data from multiple parties and then anonymizing it before publishing for data mining. In such scenarios, the data users may have a strong demand to measure the utility of the published data since most anonymization techniques have side effects on data utility. Nevertheless, this task is non-trivial because the utility measuring usually requires the aggregated raw data, which is not revealed to the data users due to privacy concerns. What’s worse, the data publishers may even cheat in the raw data since no one including the individual providers knows the full dataset.
In this paper, we first propose a privacy-preserving utility verification mechanism based upon cryptographic technique for DiffPart – a differentially private scheme designed for set-valued data. Privacy-Preserving Of Data Published By Non-Interactive Private Mechanisms This proposal can measure the data utility based upon the encrypted frequencies of the aggregated raw data instead of the plain values, which thus prevents privacy breach. Moreover, it is enabled to privately check the correctness of the encrypted frequencies provided by the publisher, which helps detect dishonest publishers. We also extend this mechanism to DiffGen – another differentially private publishing scheme designed for relational data. Our theoretical and experimental evaluations demonstrate the security and efficiency of the proposed mechanism.
Software Requirements: –
Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script
Android SDK – adt-bundle-windows-x86
IDE: Eclipse Mars
- A lot of privacy models and corresponding anonymization mechanisms have been proposed in the literature such as k-anonymity and differential privacy.
- k-anonymity and its variants (e.g. l-diversity and t-closeness protect privacy by generalizing the records such that they cannot be distinguished from some other records. Differential privacy is a much more rigorous privacy model. It requires that the released data is insensitive to the addition or removal of a single record.
- We first propose a privacy-preserving utility verification mechanism for DiffPart, a differentially private anonymization algorithm designed for set-valued data.
- DiffPart perturbs the frequencies of the records based on a context-free taxonomy tree and no items in the original data are generalized.
- Our proposal solves the challenge to verify the utility of the published data based on the encrypted frequencies of the original data records instead of their plain values. As a result, it can protect the original data from the verifying parties (i.e., the data users) because they cannot learn whether or how many times a specific record appears in the raw dataset without knowing its real frequency. In addition, since the encrypted frequencies are provided by the publisher, we also present a scheme for the verifying parties to incrementally verify its correctness.
- We then extend the above mechanism to DiffGen, a differentially private anonymization algorithm designed for relational data. Different from DiffPart, DiffGen may generalize the attribute values before perturbing the frequency of each record. Information losses are caused by both the generalization and the perturbation. These two kinds of information losses are measured separately by distinct utility metrics.We take both into consideration.
- Our analysis shows that the utility verification for generalization operations can be carried out with only the published data. As a result, this verification does not need any protection. The utility metric for the perturbation is similar with that for DiffPart.We thus adapt the proposed privacy-preserving mechanism to this verification.
We conduct a series of experiments upon the real world set-valued data and relational data to evaluate the efficiency of the proposed mechanisms. The results show that these mechanisms are efficient enough provided that both the data publishing and utility verification are offline.
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