Sedic privacy-aware data intensive computing on hybrid clouds pdf

Sedic 7, a secure data intensive computing system which can not only automatically partition a computing job according to the security levels of the data it works on, but also arrange. In this paper, we present a suite of new techniques that make such privacyaware data intensive computing possible. Pdf demo yousra aafer, nan zhang cofirst author, zhongwen zhang, xiao zhang, kai chen, xiaofeng wang, xiaoyong zhou, wenliang du, michael grace, hare hunting in the wild android. Yousra aafer, nan zhang cofirst author, zhongwen zhang, xiao zhang, kai chen, xiaofeng wang, xiaoyong zhou, wenliang du, michael grace, hare hunting in the wild android. In this paper, we present a suite of new techniques that make such privacy aware data intensive computing possible. Proceedings of the 18th acm conference on computer and communications security. Journal of application research of computers, september, 2007. These theme areas are certainly not inclusive, rather indicative of the wide variety to which big data now occupies decision analytics. A framework for privacyaware computing on hybrid clouds with. To do so, the attacker colludes with all storage servers, nontarget users, and up to t1 key servers. Secure and efficient query processing over hybrid clouds. We present sparsi, a novel theoretical framework for partitioning sensitive data across multiple noncolluding adversaries.

Privacyaware relationship semanticsbased xacml access. Cloud computing is the use of computing resources hardware and software that are delivered as a service over a network typically the internet. Keywordshybrid clouds, privacy, data sensitivity, tagging. Privacy preserving data offloading based on transformation. Data analytics lifecycle processes emc corporation. However, current cloud frameworks, such as mapreduce 12, do not support privacy aware data operations such as data search and retrieval in hybrid cloud. A hybrid cloud approach for secure authorized deduplication. In proceedings of the 18th acm conference on computer and communications security ccs 11.

The system, called sedic, leverages the special features of mapreduce to automatically partition a. Our system, called sedic, includes a privacy aware execution framework that automatically partitions a computing job according to the security levels of the data it involves, and distributes the computation between the public and private clouds. A study on authorized deduplication techniques in cloud computing. Most work in privacyaware data sharing has considered disclosing summaries. Data deduplication is the technique which compresses the data by removing the duplicate copies of identical data and it is extensively used in cloud storage to save bandwidth and minimize the storage space. An example for hybrid cloud is the data stored in private cloud of a travel. Our system, called sedic, leverages the special features of mapreduce to. Optimized scheduling for better data anonymization in cloud. External integrity verification for outsourced big data in. Vairavel published on 20180730 download full article with reference data and citations. A framework for privacy aware computing on hybrid clouds with mixedsensitivity data abstract by xiangqiang xu, m.

Selected publications chinese university of hong kong. Proceedings of the 18th acm conference on computer and. Secure and authorized model for singleinstance data storage. Largescale privacypreserving mapping of human genomic. Therefore, mapreduce which is a programming framework for preparing and creating huge data collections should be. The hybrid cloud has already been adopted by most organizational cloud users and is still undergoing a rapid development, with new techniques mushroomed to enable a smoother inter cloud coordination e. In proceedings of the 18th acm conference on computer and communications security. Corporate and private users outsource their data to.

Our system, called sedic, leverages the special features of mapreduce to automatically partition a computing job according to the security levels of the data it works on, and arrange the computation across a hybrid cloud. To secure the confidentiality of sensitive data during deduplication, the convergent encryption technique is used to encrypt the data before outsourcing. Optimized homomorphic scheme on map reduce for data privacy. Xinghui zhao cloud computing has signi cantly increased the computationstorage capacity for regular users, which leads to the popularity of transplanting largescale computations, most. Nonetheless, there are applications, including online advertising, cloud computing and. Executing data analytics tasks in mapreduce systems introduces new security and privacy concerns as the processed unstructured datasets may contain sensitive information e. Request pdf privacy preserving data offloading based on transformation mobile cloud. A study on the threat of hanging attribute references. Selected publications 2019 fenghao xu, wenrui diao, zhou li, jiongyi chen, kehuan zhang. A study on authorized deduplication techniques in cloud. A trusted third party based trusted verifier for multilayered outsourced big data system in cloud environment jing zhan 1,2,3, xudong fan 1, lei cai 1, yaqi gao 1, junxi zhuang 1 1 college of computer science, faculty of information technology, beijing university of technology, beijing, china.

Our system, called sedic, includes a privacyaware execution framework that automatically partitions a computing job according to the security levels of the data it. Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data intensive applications without infrastructure investment. Optimized scheduling for better data anonymization in. Enabling costeffective privacy preserving of intermediate. Sedic provides a privacyaware hybrid computing paradigm sedic schedules maps such that tasks on private clouds operate on sensitive data while tasks on public clouds operate on nonsensitive data sedic automatically extracts combiners from reduce functions that allow public clouds to process data. However, current cloud frameworks, such as mapreduce 12, do not support privacyaware data operations such as data search and retrieval in. Zhang, kehuan and zhou, xiaoyong and chen, yangyi and wang, xiaofeng. Yousra aafer, nan zhang, zhongwen zhang, xiao zhang, kai chen, xiaofeng wang, xiaoyong zhou, wen liang du, michael grace. A hybrid cloud is a combined form of private clouds and public clouds in which. In this paper, we propose a unique hybrid cloud platform called vncache that. However, this hybrid cloud computing is not supported by todays dataintensive computing frameworks, mapre duce in particular, which forces the users to. Breaking android security mechanisms via malicious bluetooth peripherals. The emergence of cloud computing has simplified the flow of largescale deployment distributed system of software suppliers.

Hybrid clouds provide potentials for handling data separately. A trusted third party based trusted verifier for multilayered outsourced big data system in cloud environment jing zhan 1,2,3, xudong fan 1, lei cai 1, yaqi gao 1, junxi zhuang 1 1 college of. Security and privacy have long been the primary concerns of cloud computing platforms. The paper postulates a risk aware approach to partitioning computation over hybrid clouds that provides an abstraction to address secure cloud data processing in a variety of system and application contexts. Sedic aims to assure high privacy which only allows nonsensitive data to be. Optimized homomorphic scheme on map reduce for data. Data stream based real time network traffic data analysis system design. Cloud computing provides massive computation power and storage capacity which enable users to deploy. Stateoftheart progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. In this day and age, there is a disruptive shift in technology concerning the powerful ability of cloud storage that back up and store large data in the cloud, as well as syncing it across multiple devices. Usually, dataintensive applications involve both public and private data. With the advent of cloud computing, data owner is motivated to outsource their data to the cloud platform for great flexibility and economic savings. Sedic provides a privacyaware hybrid computing paradigm sedic schedules maps such that tasks on private clouds operate on sensitive data while tasks on public clouds operate on nonsensitive data.

Corporate and private users outsource their data to cloud storage providers, recent data breach incidents make endtoend encryption an increasingly prominent requirement. To appear in proceedings of the 26th annual network and distributed system security symposium ndss 2019. In this threat model, an attacker wants to break data confidentiality of a target user. Thereby, they work on and then assign the computation without sensitive data to a public cloud. Secure and authorized model for singleinstance data. An example for hybrid cloud is the data stored in private cloud of a travel agency that is manipulated by a program running in the public cloud. Risk aware approach to data confidentiality in cloud computing. Data deduplication, convergent encryption, confidentiality, hybrid cloud, authorized.

Mapreduce analysis for cloudarchived data school of computing. Privacyaware data intensive computing on hybrid clouds, in. A secure authorized deduplication using hybrid cloud approach. In acm conference on computer and communications security ccs, 2015. However, this hybrid cloud computing is not supported by todays data intensive computing frameworks, mapreduce in particular, which forces the users to manually split their computing tasks. We consider data confidentiality for both data storage and data forwarding.

Cloud computingbased mapmatching for transportation data. The system, called sedic, leverages the special features of mapreduce to automatically partition a computing job according to the security levels of the data it works on, and arrange the computation across a hybrid cloud. A novel cloud computing algorithm of security and privacy. Security insurance is a paramount cloud services issue in the most recent decade. Composabilityofcryptographic protocols compositiontheoremswithout preestablished session identifiers 41 ralfkilsters, maxtuengerthaluniversityoftrier composabilityofbellarerogawaykeyexchangeprotocols 51. This entry was posted in icact 2016 volume 4 issue 22. Most work in privacy aware data sharing has considered disclosing summaries where the aggregate information about the data is preserved, but sensitive user information is protected. Computational integrity in hybrid cloud andrew pawloski, longfei wu, xiaojiang du. Cloudcomputing proofsofownershipin remotestoragesystems 491 shai halevi ibmt. Analysis of cloud storage and its data security scientific. Towards data confidentiality and a vulnerability analysis. Sedic 51 does not o er the same tagging granularity, but proposes to automatically modify reducers to optimize the data transfers in a hybrid cloud. On the other hand, existing secure hybrid cloud solutions do not provide a data partition method, and they require users to manually split.

In this paper, we propose a privacyaware framework on hybrid clouds to guarantee data privacy by segregating the sensitive data from the rest, and processing the sensitive data on the private cloud only. The four themes are largescale data analytics and cloud computing, computational biology, health informatics, and interactive content analytics. In this day and age, there is a disruptive shift in technology concerning the powerful ability of cloud storage that back up and store large data in the cloud, as well as syncing it across multiple devices automatically, and its drawbacks in data security. In this paper, we present a suite of new techniques that make such privacyaware dataintensive computing possible. While the tfidf workflow is reasonably computeintensive, the facebook workflow is. Optimized scheduling for better data anonymization in cloud using top down specialization written by c. It also presents a new opportunity that makes practical, secure outsourcing of computation tasks possible. Hybrid clouds offer an opportunity to selectively outsource data and computation based on the level of sensitivity involved. Security and privacy issues in cloud computing final. Data deduplication is the technique which compresses the data by removing the duplicate copies of identical data and it is extensively used in cloud storage to save bandwidth and minimize the storage. Privacyaware data intensive computing on hybrid clouds. A hybrid cloud is a combined form of private clouds and public clouds in which some critical data resides in the enterprises private cloud while other data is stored in and accessible from a public cloud. This position paper is based on a major cooperative research and development proposal to form a big data research, analytics, and information network brain. Watsonresearchcenter, dannyhamikibmhaifaresearchlabs, benny pinkas barhanuniversity.