Efficient Q-Value Zero-Leakage Protection Scheme in SRS Regularly Publishing Private Data

Abstract

Spontaneous Reporting System (SRS) has been widely established to collect adverse drug events. Thus, SRS promotes the detection and analysis of ADR (adverse drug reactions), such as the FDA Adverse Event Reporting System (FAERS). The SRS data needs to be provided to researchers. Meanwhile, the SRS data is publicly available to facilitate the study of ADR detection and analysis. In general, SRS data contains private information of some individual characteristics. Before the information is published, it is necessary to anonymize private information in the SRS data to prevent disclosure of individual privacy. There are many privacy protection methods. The most classic method for protecting SRS data is called as PPMS. However, in the real world, SRS data is growing dynamically and needs to be published regularly. In this case, PPMS has some shortcomings in the memory consumption, anonymity efficiency, data update and data security. To remove these shortcomings, we propose an Efficient Q-value Zero-leakage protection Scheme in SRS regularly publishing private data, called EQZS. EQZS can deal with almost all of potential attacks. Meanwhile, EQZS removes the shortcomings of PPMS. The experimental results show that our scheme EQZS solves the problem of privacy leakage in SRS regularly publishing private data. Meanwhile, EQZS significantly outperforms PPMS on the efficiency of memory consumption, privacy anonymity and data update.

Improved privacy preserving method for periodical SRS publishing

“…The traditional periodical data publishing mechanisms [ 9 – 14 ] are not suitable for the SRS data due to its some special features. The existing SRS data publishing methods [ 22 – 25 ] had considered these types of attacks in the scenario of SRS data publishing: B -attack, F -attack and L -attack, trying to guarantee the information usability of the released tables. However, the SRS data has some special features, such as identity preservation and multivalued sensitive attributes, which make it more vulnerable to security threats than the other types of data.…”

“…PPMS(k, θ � )-bounding has not considered medicine discontinuation and records with massive symptoms, hence privacy may be disclosed by an adversary. As the extended versions of PPMS(k, θ � )-bounding, the other existing SRS data publishing methods [23][24][25] have not considered the attacks described by example 1 and example 2, either. It is necessary to find a way to defend against the above attacks.…”

“…This method can defend against the three attack methods(B-attack, F-attack and L-attack) which are based on correlations among different released tables. After that, several ADEs data publishing methods based on PPMS(k, θ � )-bounding were presented [23][24][25]. Hsiao et al [23] presented a privacy model, called Closed l-diversity, to process the missing value by guaranteeing that each partial QID-group includes at least l different sensitive values.…”

“…They also proposed an algorithm, called Closed l-diversification, to achieve Closed l-diversity. Cui et al [25] presented a SRSs data publication approach, called EQZS, to improve the efficiency of PPMS(k, θ � )-bounding.…”

“…They also proposed an algorithm, called Closed l -diversification, to achieve Closed l -diversity. Cui et al [ 25 ] presented a SRSs data publication approach, called EQZS, to improve the efficiency of PPMS( k , θ *)-bounding. The new values and old values covered each other in this method, which resulted in the limitation of the released data usability.…”