7,980 research outputs found
Sharing Computer Network Logs for Security and Privacy: A Motivation for New Methodologies of Anonymization
Logs are one of the most fundamental resources to any security professional.
It is widely recognized by the government and industry that it is both
beneficial and desirable to share logs for the purpose of security research.
However, the sharing is not happening or not to the degree or magnitude that is
desired. Organizations are reluctant to share logs because of the risk of
exposing sensitive information to potential attackers. We believe this
reluctance remains high because current anonymization techniques are weak and
one-size-fits-all--or better put, one size tries to fit all. We must develop
standards and make anonymization available at varying levels, striking a
balance between privacy and utility. Organizations have different needs and
trust other organizations to different degrees. They must be able to map
multiple anonymization levels with defined risks to the trust levels they share
with (would-be) receivers. It is not until there are industry standards for
multiple levels of anonymization that we will be able to move forward and
achieve the goal of widespread sharing of logs for security researchers.Comment: 17 pages, 1 figur
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201
Privacy Preservation by Disassociation
In this work, we focus on protection against identity disclosure in the
publication of sparse multidimensional data. Existing multidimensional
anonymization techniquesa) protect the privacy of users either by altering the
set of quasi-identifiers of the original data (e.g., by generalization or
suppression) or by adding noise (e.g., using differential privacy) and/or (b)
assume a clear distinction between sensitive and non-sensitive information and
sever the possible linkage. In many real world applications the above
techniques are not applicable. For instance, consider web search query logs.
Suppressing or generalizing anonymization methods would remove the most
valuable information in the dataset: the original query terms. Additionally,
web search query logs contain millions of query terms which cannot be
categorized as sensitive or non-sensitive since a term may be sensitive for a
user and non-sensitive for another. Motivated by this observation, we propose
an anonymization technique termed disassociation that preserves the original
terms but hides the fact that two or more different terms appear in the same
record. We protect the users' privacy by disassociating record terms that
participate in identifying combinations. This way the adversary cannot
associate with high probability a record with a rare combination of terms. To
the best of our knowledge, our proposal is the first to employ such a technique
to provide protection against identity disclosure. We propose an anonymization
algorithm based on our approach and evaluate its performance on real and
synthetic datasets, comparing it against other state-of-the-art methods based
on generalization and differential privacy.Comment: VLDB201
Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks
Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to
meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information
to multiple sinks and reduce energy consumption
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