1,277 research outputs found
IoT Expunge: Implementing Verifiable Retention of IoT Data
The growing deployment of Internet of Things (IoT) systems aims to ease the
daily life of end-users by providing several value-added services. However, IoT
systems may capture and store sensitive, personal data about individuals in the
cloud, thereby jeopardizing user-privacy. Emerging legislation, such as
California's CalOPPA and GDPR in Europe, support strong privacy laws to protect
an individual's data in the cloud. One such law relates to strict enforcement
of data retention policies. This paper proposes a framework, entitled IoT
Expunge that allows sensor data providers to store the data in cloud platforms
that will ensure enforcement of retention policies. Additionally, the cloud
provider produces verifiable proofs of its adherence to the retention policies.
Experimental results on a real-world smart building testbed show that IoT
Expunge imposes minimal overheads to the user to verify the data against data
retention policies.Comment: This paper has been accepted in 10th ACM Conference on Data and
Application Security and Privacy (CODASPY), 202
Split keyword fuzzy and synonym search over encrypted cloud data
A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is search based on keywords and retrieve interested files by data user using a lot of traditional Search scheme. Existing search schemes supports exact keyword match or fuzzy keyword search, but synonym based multi-keyword search are not supported. In the real world scenario, cloud users may not know the exact keyword for searching and they might give synonym of the keyword as the input for search instead of exact or fuzzy keyword due to lack of appropriate knowledge of data. In this paper, we describe an efficient search approach for encrypted data called as Split Keyword Fuzzy and Synonym Search (SKFS). Multi-keyword ranked search with accurate keyword and Fuzzy search supports synonym queries are a major contribution of SKFS. The wildcard Technique is used to store the keywords securely within the index tree. Index tree helps to search faster, accurate and low storage cost. Extensive experimental results on real-time data sets shows, the proposed solution is effective and efficient for multi-keyword ranked search and synonym queries Fuzzy based search over encrypted cloud data. © 2017 Springer Science+Business Media, LL
A Survey on Privacy Preserving Data Aggregation Protocols forWireless Sensor Networks
The data aggregation is a widely used mechanism in Wireless Sensor Networks (WSNs) to increase lifetime of a sensor node, send robust information by avoiding redundant data transmission to the base station. The privacy preserving data aggregation is a challenge in wireless communication medium as it could be eavesdropped; however it enhances the security without compromising energy efficiency. Thus the privacy protecting data aggregation protocols aims to prevent the disclosure of individual data though an adversary intercept a link or compromise a node’s data. We present a study of different privacy preserving data aggregation techniques used in WSNs to enhance energy and security based on the types of nodes in the network, topology and encryptions used for data aggregation.</p
POPE: Partial Order Preserving Encoding
Recently there has been much interest in performing search queries over
encrypted data to enable functionality while protecting sensitive data. One
particularly efficient mechanism for executing such queries is order-preserving
encryption/encoding (OPE) which results in ciphertexts that preserve the
relative order of the underlying plaintexts thus allowing range and comparison
queries to be performed directly on ciphertexts. In this paper, we propose an
alternative approach to range queries over encrypted data that is optimized to
support insert-heavy workloads as are common in "big data" applications while
still maintaining search functionality and achieving stronger security.
Specifically, we propose a new primitive called partial order preserving
encoding (POPE) that achieves ideal OPE security with frequency hiding and also
leaves a sizable fraction of the data pairwise incomparable. Using only O(1)
persistent and non-persistent client storage for
, our POPE scheme provides extremely fast batch insertion
consisting of a single round, and efficient search with O(1) amortized cost for
up to search queries. This improved security and
performance makes our scheme better suited for today's insert-heavy databases.Comment: Appears in ACM CCS 2016 Proceeding
- …