6 research outputs found
Evaluating privacy-preserving record linkage using cryptographic long-term keys and multibit trees on large medical datasets.
Background: Integrating medical data using databases from different sources by record linkage is a powerful technique increasingly used in medical research. Under many jurisdictions, unique personal identifiers needed for linking the records are unavailable. Since sensitive attributes, such as names, have to be used instead, privacy regulations usually demand encrypting these identifiers. The corresponding set of techniques for privacy-preserving record linkage (PPRL) has received widespread attention. One recent method is based on Bloom filters. Due to superior resilience against cryptographic attacks, composite Bloom filters (cryptographic long-term keys, CLKs) are considered best practice for privacy in PPRL. Real-world performance of these techniques using large-scale data is unknown up to now. Methods: Using a large subset of Australian hospital admission data, we tested the performance of an innovative PPRL technique (CLKs using multibit trees) against a gold-standard derived from clear-text probabilistic record linkage. Linkage time and linkage quality (recall, precision and F-measure) were evaluated. Results: Clear text probabilistic linkage resulted in marginally higher precision and recall than CLKs. PPRL required more computing time but 5 million records could still be de-duplicated within one day. However, the PPRL approach required fine tuning of parameters. Conclusions: We argue that increased privacy of PPRL comes with the price of small losses in precision and recall and a large increase in computational burden and setup time. These costs seem to be acceptable in most applied settings, but they have to be considered in the decision to apply PPRL. Further research on the optimal automatic choice of parameters is needed
SFour: A Protocol for Cryptographically Secure Record Linkage at Scale
The prevalence of various (and increasingly large) datasets presents the challenging problem of discovering common entities dispersed across disparate datasets. Solutions to the private record linkage problem (PRL) aim to enable such explorations of datasets in a secure manner.
A two-party PRL protocol allows two parties to determine for which entities they each possess a record (either an exact matching record or a fuzzy matching record) in their respective datasets — without revealing to one another information about any entities for which they do not both possess records. Although several solutions have been proposed to solve the PRL problem, no current solution offers a fully cryptographic security guarantee while maintaining both high accuracy of output and subquadratic runtime efficiency.
To this end, we propose the first known efficient PRL protocol that runs in subquadratic time, provides high accuracy, and guarantees cryptographic security
A Scalable Blocking Framework for Multidatabase Privacy-preserving Record Linkage
Today many application domains, such as national statistics,
healthcare, business analytic, fraud detection, and national
security, require data to be integrated from multiple databases.
Record linkage (RL) is a process used in data integration which
links multiple databases to identify matching records that belong
to the same entity. RL enriches the usefulness of data by
removing duplicates, errors, and inconsistencies which improves
the effectiveness of decision making in data analytic
applications.
Often, organisations are not willing or authorised to share the
sensitive information in their databases with any other party due
to privacy and confidentiality regulations. The linkage of
databases of different organisations is an emerging research area
known as privacy-preserving record linkage (PPRL). PPRL
facilitates the linkage of databases by ensuring the privacy of
the entities in these databases.
In multidatabase (MD) context, PPRL is significantly challenged
by the intrinsic exponential growth in the number of potential
record pair comparisons. Such linkage often requires significant
time and computational resources to produce the resulting
matching sets of records. Due to increased risk of collusion,
preserving the privacy of the data is more problematic with an
increase of number of parties involved in the linkage process.
Blocking is commonly used to scale the linkage of large
databases. The aim of blocking is to remove those record pairs
that correspond to non-matches (refer to different entities).
Many techniques have been proposed for RL and PPRL for blocking
two databases. However, many of these techniques are not suitable
for blocking multiple databases. This creates a need to develop
blocking technique for the multidatabase linkage context as
real-world applications increasingly require more than two
databases.
This thesis is the first to conduct extensive research on
blocking for multidatabase privacy-preserved record linkage
(MD-PPRL). We consider several research problems in blocking of
MD-PPRL. First, we start with a broad background literature on
PPRL. This allow us to identify the main research gaps that need
to be investigated in MD-PPRL. Second, we introduce a blocking
framework for MD-PPRL which provides more flexibility and control
to database owners in the block generation process. Third, we
propose different techniques that are used in our framework for
(1) blocking of multiple databases, (2) identifying blocks that
need to be compared across subgroups of these databases, and (3)
filtering redundant record pair comparisons by the efficient
scheduling of block comparisons to improve the scalability of
MD-PPRL. Each of these techniques covers an important aspect of
blocking in real-world MD-PPRL applications. Finally, this thesis
reports on an extensive evaluation of the combined application of
these methods with real datasets, which illustrates that they
outperform existing approaches in term of scalability, accuracy,
and privacy
Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment
This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g
Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment
This paper described an analysis the different
capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g