74,029 research outputs found
Anonymous subject identification and privacy information management in video surveillance
The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework
Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency
Recently, several practical attacks raised serious concerns over the security
of searchable encryption. The attacks have brought emphasis on forward privacy,
which is the key concept behind solutions to the adaptive leakage-exploiting
attacks, and will very likely to become mandatory in the design of new
searchable encryption schemes. For a long time, forward privacy implies
inefficiency and thus most existing searchable encryption schemes do not
support it. Very recently, Bost (CCS 2016) showed that forward privacy can be
obtained without inducing a large communication overhead. However, Bost's
scheme is constructed with a relatively inefficient public key cryptographic
primitive, and has a poor I/O performance. Both of the deficiencies
significantly hinder the practical efficiency of the scheme, and prevent it
from scaling to large data settings. To address the problems, we first present
FAST, which achieves forward privacy and the same communication efficiency as
Bost's scheme, but uses only symmetric cryptographic primitives. We then
present FASTIO, which retains all good properties of FAST, and further improves
I/O efficiency. We implemented the two schemes and compared their performance
with Bost's scheme. The experiment results show that both our schemes are
highly efficient, and FASTIO achieves a much better scalability due to its
optimized I/O
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
Privacy-Preserving Shortest Path Computation
Navigation is one of the most popular cloud computing services. But in
virtually all cloud-based navigation systems, the client must reveal her
location and destination to the cloud service provider in order to learn the
fastest route. In this work, we present a cryptographic protocol for navigation
on city streets that provides privacy for both the client's location and the
service provider's routing data. Our key ingredient is a novel method for
compressing the next-hop routing matrices in networks such as city street maps.
Applying our compression method to the map of Los Angeles, for example, we
achieve over tenfold reduction in the representation size. In conjunction with
other cryptographic techniques, this compressed representation results in an
efficient protocol suitable for fully-private real-time navigation on city
streets. We demonstrate the practicality of our protocol by benchmarking it on
real street map data for major cities such as San Francisco and Washington,
D.C.Comment: Extended version of NDSS 2016 pape
A comparison of standard spell checking algorithms and a novel binary neural approach
In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. In this paper, we evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost
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