.png){kind=icon}
.png){kind=icon}
Return
Accurate and efficient privacy-preserving string matching
.png){kind=icon}
OA
AI
DOI：10.1007/s41060-022-00320-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The task of calculating similarities between strings held by different organisations without revealing these strings is an increasingly important problem in areas such as health informatics, national censuses, genomics, and fraud detection. Most existing privacy-preserving string matching approaches are either based on comparing sets of encoded characters allowing only exact matching of encoded strings, or they are aimed at long genomics sequences that have a small alphabet. The set-based privacy-preserving similarity functions that are commonly used to compare name and address strings in the context of privacy-preserving record linkage do not take the positions of sub-strings into account. As a result, two very different strings can potentially be considered as a match leading to wrongly linked records. Furthermore, existing set-based techniques cannot identify the length of the longest common sub-string across two strings. In this paper, we propose two new approaches for accurate and efficient privacy-preserving string matching that provide privacy against various attacks. In the first approach we apply hashing-based encoding on sub-strings (q-grams) to compare sensitive strings, while in the second approach we generate one-bit array from the sub-strings of a string to identify the longest common bit sequences. We evaluate our approaches on several data sets with different types of strings, and validate their privacy, accuracy, and complexity compared to three baseline techniques, showing that they outperform all baselines.
Keywords:
Secure hash encoding
Bit array encoding
String comparison
Privacy-preserving record linkage
Bloom filter encoding
Journal
I
IF:
2.8
Papers:
1.0K
Citations:
1.3K