6,194 research outputs found
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
A practical and secure multi-keyword search method over encrypted cloud data
Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective
Shortest Path Computation with No Information Leakage
Shortest path computation is one of the most common queries in location-based
services (LBSs). Although particularly useful, such queries raise serious
privacy concerns. Exposing to a (potentially untrusted) LBS the client's
position and her destination may reveal personal information, such as social
habits, health condition, shopping preferences, lifestyle choices, etc. The
only existing method for privacy-preserving shortest path computation follows
the obfuscation paradigm; it prevents the LBS from inferring the source and
destination of the query with a probability higher than a threshold. This
implies, however, that the LBS still deduces some information (albeit not
exact) about the client's location and her destination. In this paper we aim at
strong privacy, where the adversary learns nothing about the shortest path
query. We achieve this via established private information retrieval
techniques, which we treat as black-box building blocks. Experiments on real,
large-scale road networks assess the practicality of our schemes.Comment: VLDB201
Octopus: A Secure and Anonymous DHT Lookup
Distributed Hash Table (DHT) lookup is a core technique in structured
peer-to-peer (P2P) networks. Its decentralized nature introduces security and
privacy vulnerabilities for applications built on top of them; we thus set out
to design a lookup mechanism achieving both security and anonymity, heretofore
an open problem. We present Octopus, a novel DHT lookup which provides strong
guarantees for both security and anonymity. Octopus uses attacker
identification mechanisms to discover and remove malicious nodes, severely
limiting an adversary's ability to carry out active attacks, and splits lookup
queries over separate anonymous paths and introduces dummy queries to achieve
high levels of anonymity. We analyze the security of Octopus by developing an
event-based simulator to show that the attacker discovery mechanisms can
rapidly identify malicious nodes with low error rate. We calculate the
anonymity of Octopus using probabilistic modeling and show that Octopus can
achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab
with 207 nodes and show that Octopus has reasonable lookup latency and
manageable communication overhead
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A probability based hybrid energy-efficient privacy preserving scheme to encounter with wireless traffic snooping in smart home
Application of pervasive computing devices in smart homes are rising sharply and due to this matter, demands for efficient privacy protection are increasing urgently. Possibility of interference in wireless networks is proved by previous work. Adversaries can discover contextual information because of traffic monitoring and classifying transmitters based on their radio fingerprints while data packets are encrypted or content is not important for attackers. To conceal communication patterns various approaches have been investigated. They are mainly based on injection of dummy packets into the network traffic and adding delay to transmission time. In this paper, we introduce a hybrid energy-efficient privacy preserving scheme for generating and sending dummy packets through a decision-making algorithm which works based on probability to maximize confusion of attacker in clarifying the real pattern of network traffi
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