Novel Method to Watermark Anonymized Data for Data Publishing

Nakamura, Yuichi; Nakatsuka, Yoshimichi; Nishi, Hiroaki

doi:10.1587/transinf.2016icp0015

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the efficacy of the proposed method, we implemented it using Python 3.6.4. In addition, we implemented our previous method [11] for comparison.…”

Section: Discussionmentioning

confidence: 99%

“…The extracted information was compared with the original information to check whether the two pieces of information were identical. This metric is stricter than that of our previous evaluation in [11]. We executed this procedure 50 times for each attack type and attack ratio.…”

Section: Protection From Distortion Attacksmentioning

confidence: 99%

“…In our previous study, a digital watermarking method for anonymized data was proposed [11]. The previously proposed digital watermarking method anonymizes further to embed digital watermarks in the anonymized data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Watermarking for Anonymized Data With Low Information Loss

Nakamura

Nishi

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

At present, massive amounts of data are utilized for artificial intelligence technologies such as machine learning and deep learning. However, these data must be utilized carefully while preserving data privacy. Data anonymization is a technique enabling both data mining and privacy protection, preventing the identification of individuals by generalizing the data to include multiple records with the same values. In this study, we consider a data-publishing infrastructure for personal data sharing. The infrastructure anonymizes data prior to publishing it to users for privacy protection; however, the problem of unauthorized republishing by malicious users must be considered. To address this issue, we studied digital watermarking methods that correlate data users with anonymized data. Our previous method embedded information indicating the original user to detect illegally republished data. However, this method did not focus on information loss. This study proposes another digital watermarking method for anonymized data that achieves low information loss. The proposed method replaces values in tuples to embed information. To reduce the information loss caused by the embedding, the proposed method selects replacement values from the candidates whose meanings are similar to the original. We propose the use of vector-conversion tables to select replacement values. The proposed method also extends the maximum length of the embedded bit string by embedding multiple bits into a single tuple. Moreover, we measured the tolerance to distortion attacks to evaluate the efficacy of the proposed method. The proposed method is non-blind, i.e., data prior to digital watermarking is required to perform extraction.

show abstract

“…To evaluate the efficacy of the proposed method, we implemented it using Python 3.6.4. In addition, we implemented our previous method [11] for comparison.…”

Section: Discussionmentioning

confidence: 99%

Section: Protection From Distortion Attacksmentioning

confidence: 99%