A New Data Collection Technique for Preserving Privacy

Abstract

Abstract.A major obstacle that hinders medical and social research is the lack of reliable data due to people's reluctance to reveal private information to strangers. Fortunately, statistical inference always targets a well-defined population rather than a particular individual subject and, in many current applications, data can be collected using a web-based system or other mobile devices. These two characteristics enable us to develop a data collection method, called triple matrix-masking (TM 2 ), which offers strong privacy protection with an immediate matrix transformation so that even the researchers cannot see the data, and then further uses matrix transformations to guarantee that the data will still be analyzable by standard statistical methods. The entities involved in the proposed process are a masking service provider who receives the initially masked data and then applies another mask, and the data collectors who partially decrypt the now doubly masked data and then apply a third mask before releasing the data to the public. A critical feature of the method is that the keys to generate the matrices are held separately. This ensures that nobody sees the actual data, but because of the specially designed transformations, statistical inference on parameters of interest can be conducted with the same results as if the original data were used. Hence the TM 2 method hides sensitive data with no efficiency loss for statistical inference of binary and normal data, which improves over Warner's randomized response technique. In addition, we add several features to the proposed procedure: an error checking mechanism is built into the data collection process in order to make sure that the masked data used for analysis are an appropriate transformation of the original data; and a partial masking technique is introduced to grant data users access to non-sensitive personal information while sensitive information remains hidden.

Applying Triple-Matrix Masking for Privacy Preserving Data Collection and Sharing in HIV Studies

“…A new method called triple-matrix masking is developed by Wu et al [1]. It advances the data masking from centralized data centers all the way to patients themselves in order to achieve a full privacy protection.…”

“…Step 3. The masking service provider chooses a different key 536, and uses the Matlab program described in the Appendix 1 of Wu et al [1] to generate a 30 by 30 random orthogonal matrix A 2 = GenerateROM(536, 30). Due to space limit, we omit the A 2 matrix here but readers can easily get the matrix by running the Matlab program.…”

“…These two numbers, along with the Table 3). In addition, it has been shown in [1] that any multivariate regression analysis (e.g., Log10PVL predicted by age and CD4 counts) based on the masked data will also give the same results as if the original data are used.…”

“…The relatively high rate of missing or skewed information during data collection can potentially cause bias in HIV research. To address the privacy issue, this paper adapts a generic privacy-preserving data collection method, called triple-matrix masking and proposed by Wu et al (2014) [1] to the context of HIV studies with domain-specific considerations in an effort to relieve the privacy concern and encourage patients to report truthful data in HIV research. The proposed approach enhances current practice with new technologies for full privacy protection, ensuring that raw data stay with patients and only masked data are collected.…”

Collusion Resistant Multi-Matrix Masking for Privacy-Preserving Data Collection

“…The rest of the paper is organized as follows. In Section 2, we summarize the related existing methods for privacy protection, including Warner's randomized response technique [4], the item count technique [6], the item sum technique [7] and the triple matrix-masking (TM 2 ) method introduced by Wu et al [8]. In Section 3, we propose a collusion resistant data collection method, which overcomes the limitations of the existing methods and provides a more secure and trustworthy data collection environment.…”