900 research outputs found
k-anonymous Microdata Release via Post Randomisation Method
The problem of the release of anonymized microdata is an important topic in
the fields of statistical disclosure control (SDC) and privacy preserving data
publishing (PPDP), and yet it remains sufficiently unsolved. In these research
fields, k-anonymity has been widely studied as an anonymity notion for mainly
deterministic anonymization algorithms, and some probabilistic relaxations have
been developed. However, they are not sufficient due to their limitations,
i.e., being weaker than the original k-anonymity or requiring strong parametric
assumptions. First we propose Pk-anonymity, a new probabilistic k-anonymity,
and prove that Pk-anonymity is a mathematical extension of k-anonymity rather
than a relaxation. Furthermore, Pk-anonymity requires no parametric
assumptions. This property has a significant meaning in the viewpoint that it
enables us to compare privacy levels of probabilistic microdata release
algorithms with deterministic ones. Second, we apply Pk-anonymity to the post
randomization method (PRAM), which is an SDC algorithm based on randomization.
PRAM is proven to satisfy Pk-anonymity in a controlled way, i.e, one can
control PRAM's parameter so that Pk-anonymity is satisfied. On the other hand,
PRAM is also known to satisfy -differential privacy, a recent
popular and strong privacy notion. This fact means that our results
significantly enhance PRAM since it implies the satisfaction of both important
notions: k-anonymity and -differential privacy.Comment: 22 pages, 4 figure
Differentially Private Publication of Sparse Data
The problem of privately releasing data is to provide a version of a dataset
without revealing sensitive information about the individuals who contribute to
the data. The model of differential privacy allows such private release while
providing strong guarantees on the output. A basic mechanism achieves
differential privacy by adding noise to the frequency counts in the contingency
tables (or, a subset of the count data cube) derived from the dataset. However,
when the dataset is sparse in its underlying space, as is the case for most
multi-attribute relations, then the effect of adding noise is to vastly
increase the size of the published data: it implicitly creates a huge number of
dummy data points to mask the true data, making it almost impossible to work
with.
We present techniques to overcome this roadblock and allow efficient private
release of sparse data, while maintaining the guarantees of differential
privacy. Our approach is to release a compact summary of the noisy data.
Generating the noisy data and then summarizing it would still be very costly,
so we show how to shortcut this step, and instead directly generate the summary
from the input data, without materializing the vast intermediate noisy data. We
instantiate this outline for a variety of sampling and filtering methods, and
show how to use the resulting summary for approximate, private, query
answering. Our experimental study shows that this is an effective, practical
solution, with comparable and occasionally improved utility over the costly
materialization approach
Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy
Spontaneous reporting systems (SRS) have been developed to collect adverse
event records that contain personal demographics and sensitive information like
drug indications and adverse reactions. The release of SRS data may disclose
the privacy of the data provider. Unlike other microdata, very few
anonymyization methods have been proposed to protect individual privacy while
publishing SRS data. MS(k, {\theta}*)-bounding is the first privacy model for
SRS data that considers multiple individual records, mutli-valued sensitive
attributes, and rare events. PPMS(k, {\theta}*)-bounding then is proposed for
solving cross-release attacks caused by the follow-up cases in the periodical
SRS releasing scenario. A recent trend of microdata anonymization combines the
traditional syntactic model and differential privacy, fusing the advantages of
both models to yield a better privacy protection method. This paper proposes
the PPMS-DP(k, {\theta}*, {\epsilon}) framework, an enhancement of PPMS(k,
{\theta}*)-bounding that embraces differential privacy to improve privacy
protection of periodically released SRS data. We propose two anonymization
algorithms conforming to the PPMS-DP(k, {\theta}*, {\epsilon}) framework,
PPMS-DPnum and PPMS-DPall. Experimental results on the FAERS datasets show that
both PPMS-DPnum and PPMS-DPall provide significantly better privacy protection
than PPMS-(k, {\theta}*)-bounding without sacrificing data distortion and data
utility.Comment: 10 pages, 11 figure
Using Metrics Suites to Improve the Measurement of Privacy in Graphs
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users in a graph, while graph de-anonymization aims to re-identify users. The effectiveness of anonymization and de-anonymization algorithms is usually evaluated with privacy metrics. However, it is unclear how strong existing privacy metrics are when they are used in graph privacy. In this paper, we study 26 privacy metrics for graph anonymization and de-anonymization and evaluate their strength in terms of three criteria: monotonicity indicates whether the metric indicates lower privacy for stronger adversaries; for within-scenario comparisons, evenness indicates whether metric values are spread evenly; and for between-scenario comparisons, shared value range indicates whether metrics use a consistent value range across scenarios. Our extensive experiments indicate that no single metric fulfills all three criteria perfectly. We therefore use methods from multi-criteria decision analysis to aggregate multiple metrics in a metrics suite, and we show that these metrics suites improve monotonicity compared to the best individual metric. This important result enables more monotonic, and thus more accurate, evaluations of new graph anonymization and de-anonymization algorithms
The Challenges of Effectively Anonymizing Network Data
The availability of realistic network data plays a significant role in fostering collaboration and ensuring U.S. technical leadership in network security research. Unfortunately, a host of technical, legal, policy, and privacy issues limit the ability of operators to produce datasets for information security testing. In an effort to help overcome these limitations, several data collection efforts (e.g., CRAWDAD[14], PREDICT [34]) have been established in the past few years. The key principle used in all of these efforts to assure low-risk, high-value data is that of trace anonymization—the process of sanitizing data before release so that potentially sensitive information cannot be extracted
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