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Time-Series Pattern based Effective Noise Generation for Privacy Protection on Cloud
- Chidambaram Kandavel et al, / (IJCSIT) International Journal of Computer Science and Information Technologies
"... Abstract—Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service provid ..."
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Abstract—Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer’s private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests’ occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer’s privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk. Index Terms—Cloud computing, privacy protection, noise obfuscation, noise generation, time-series pattern, cluster Ç 1
Recent Developments in Cloud Based Systems: State of Art
, 2015
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Ranked Query Assistance in Untrusted Cloud Helper without Reveal Sensitive Data
"... Now-a-days Cloud computing is become more popular as cloud infrastructure for storing the data. Data owners are stored their data in public cloud for the flexibility and cost-preserving. In data privacy bulwark, afore preserving sensitive data it is encrypted. Utilizer trust, privacy and security ar ..."
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Now-a-days Cloud computing is become more popular as cloud infrastructure for storing the data. Data owners are stored their data in public cloud for the flexibility and cost-preserving. In data privacy bulwark, afore preserving sensitive data it is encrypted. Utilizer trust, privacy and security are more concerns. Most frequently used queries for online data analytic is the range query. Consumer-centric cloud computing is utilized for development of keenly intellective electronic contrivances amalgamated with the cloud computing technologies. Different cloud accommodations are provided to the customers with the premise that an efficacious and efficient cloud search accommodation is provided. Locality-sensitive hashing is provided the approximate queries that to distributed data servers which give quandary of the imbalanced load and space inefficiency, in which limits the query precision and incurs long query latency between users and cloud servers. This type of query accommodations could be expensive for data owner. More incremented accommodations computing and cloud computing, it is possible to outsource immensely colossal databases to database accommodation providers and the providers maintain the range-query accommodation. Utilizing outsourced accommodations, the data owner can reduce the cost of storing data in cloud infrastructure. Cloud computing providing reliable, customized, and ensured computing dynamic environment for end users.
Building Confidential and Efficient Query Services in the Cloud
"... Abstract : Cloud computing infrastructures are popularly used by peoples now a day. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data's in cloud because, sometimes the data may be hack when they use in cloud unless the confi ..."
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Abstract : Cloud computing infrastructures are popularly used by peoples now a day. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data's in cloud because, sometimes the data may be hack when they use in cloud unless the confidentiality of data and secure query processing will be provided by the cloud provider. In cloud if the user can get secured query service then the efficiency of query processing will be increased and the workload of the query processing will also be saved. To provide the confidentiality and efficient query service here we proposed RASP method. RASP denotes RAndom Space Perturbation. It also combines order preserving encryption, random projection and random noise injection. In order to process the range query to kNN query here we used kNN-R algorithm. We also analyze the RASP method will secure the multidimensional range and it will increase the working process of query.
KNN-R Data Perturbation Building confidential and Efficient Query Services within the Cloud
"... Abstract- With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unle ..."
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Abstract- With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.
Ranked Query Support in Untrusted Cloud without Reveal Sensitive Data
"... Abstract: Now-a-days Cloud computing is become more popular as cloud infrastructure for storing the data. Data owners are stored their data in public cloud for the flexibility and cost-preserving. In order to providing data privacy, Clouds allow users to store data and access can be made anywhere, a ..."
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Abstract: Now-a-days Cloud computing is become more popular as cloud infrastructure for storing the data. Data owners are stored their data in public cloud for the flexibility and cost-preserving. In order to providing data privacy, Clouds allow users to store data and access can be made anywhere, any time by using any device. Highly sensitive information such as business documents, medical records and personal information may be stored in a cloud. Security and privacy are thus very important issues in cloud computing. To keep user data confidential from an untrusted Cloud Service Provider and third parties, a natural way is encryption. The data decryption key should be disclosed only to users who have been authorized. Users can search their files using keywords in the cloud. In existing literature many schemes have been proposed. In this paper, a new technique is described: Ranked Query support in Untrusted Cloud without Reveal Sensitive Data. Which performs operations on encrypted data which will provide results without decrypting that data? It provides privacy for user querying patterns and user data. It allows Cloud Service Providers to perform operations on the encrypted data. The Cloud Service Provider is unaware of the files and keywords stored in the cloud. Ranking is used for efficient and fast retrieval of the desired files. Ranks will be assigned to files
Building Confidential & Efficient Query Services in the Cloud with RASP Perturbation
"... Abstract:- In this paper Cloud computing infrastructures are popularly used by peoples now a days. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data’s in cloud because, some-times the data may be hack when they use in cloud unless ..."
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Abstract:- In this paper Cloud computing infrastructures are popularly used by peoples now a days. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data’s in cloud because, some-times the data may be hack when they use in cloud unless the confidentiality of data and secure query processing will be provided by the cloud provider.However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. The random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query ser-vices for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensio-nality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data
kNN-R:Building Confidential and Efficient Query Services in the Cloud Using RASP Data Perturbation
"... Abstract: In this paper, the outsourcing data-intensive services to service providers is increasingly popular with the great advantages of saving hardware and software maintenance cost. Range query and k nearest neighbors (kNN) search on large-scale databases are the important data services to many ..."
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Abstract: In this paper, the outsourcing data-intensive services to service providers is increasingly popular with the great advantages of saving hardware and software maintenance cost. Range query and k nearest neighbors (kNN) search on large-scale databases are the important data services to many applications, from location-based services (LBS), machine learning, to similarity search in multimedia database. Once the kNN query service is outsourced, data confidentiality and query privacy become the important issues, because the data owner loses the control over the data. Adversaries, such as curious service providers, will try to breach the content of the database or intercept users queries to breach users privacy, especially when the queries are location queries. This security requirement dramatically increases the complexity of constructing a practical outsourced database services.
Efficient K-Nearest Neighbours Discovery using Complex Secure Evaluation Function in Geo Tagged
"... The materialization of mobile equipment with fast Internet connectivity and geo-tagged capabilities has led to a revolution in personalized position-based services where clients are enabled to admittance information about points of interest that are pertinent to their interests and are also close t ..."
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The materialization of mobile equipment with fast Internet connectivity and geo-tagged capabilities has led to a revolution in personalized position-based services where clients are enabled to admittance information about points of interest that are pertinent to their interests and are also close to their geo tagged coordinates. The important type of queries that absorb location attributes is symbolized by nearest-neighbor queries, where a client wants to retrieve the k-Point of interests that are nearest to the user’s current location. Entities specialized in various areas of interest and gather large amounts of geo-tagged data that appeal to subscribed users. In this paper the proposes development and implementation of frame work to provide more complex protected evaluation method on cipher texts, like skyline queries and security protection guarantees against the client, to prevent it from learning anything other than the received k query results.
RASP-QS: Efficient and Confidential Query Services in the
"... Hosting data query services in public clouds is an attrac-tive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confiden-tial ..."
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Hosting data query services in public clouds is an attrac-tive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confiden-tial range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloud-based computing, while enabling much faster query pro-cessing compared to the encryption-based approach. This demonstration will allow users to more intuitively under-stand the technical merits of the RASP approach via inter-active exploration of the visual interface. 1.
