Secret Sharing for Cloud Data Security

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm

fVSS: A New Secure and Cost-Efficient Scheme for Cloud Data Warehouses

Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this pa-per, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud ser-vice providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data ware-house when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balanc-ing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs

Approches de partage de clés secrètes pour la sécurisation des entrepôts de données et de l’analyse en ligne dans le nuage

Les systèmes d’information décisionnels dans le cloud Computing sont des solutions de plus en plus répandues. En effet, ces dernières offrent des capacités pour l’aide à la décision via l’élasticité des ressources pay-per-use du Cloud. Toutefois, les questions de sécurité des données demeurent une des principales préoccupations notamment lorsqu'il s’agit de traiter des données sensibles de l’entreprise. Beaucoup de questions de sécurité sont soulevées en terme de stockage, de protection, de disponibilité, d'intégrité, de sauvegarde et de récupération des données ainsi que des transferts des données dans un Cloud public. Les risques de sécurité peuvent provenir non seulement des fournisseurs de services de cloud computing mais aussi d’intrus malveillants. Les entrepôts de données dans les nuages devraient contenir des données sécurisées afin de permettre à la fois le traitement d'analyse en ligne hautement protégé et efficacement rafraîchi. Et ceci à plus faibles coûts de stockage et d'accès avec le modèle de paiement à la demande. Dans cette thèse, nous proposons deux nouvelles approches pour la sécurisation des entrepôts de données dans les nuages basées respectivement sur le partage vérifiable de clé secrète (bpVSS) et le partage vérifiable et flexible de clé secrète (fVSS). L’objectif du partage de clé cryptée et la distribution des données auprès de plusieurs fournisseurs du cloud permet de garantir la confidentialité et la disponibilité des données. bpVSS et fVSS abordent cinq lacunes des approches existantes traitant de partage de clés secrètes. Tout d'abord, ils permettent le traitement de l’analyse en ligne. Deuxièmement, ils garantissent l'intégrité des données à l'aide de deux signatures interne et externe. Troisièmement, ils aident les utilisateurs à minimiser le coût de l’entreposage du cloud en limitant le volume global de données cryptées. Sachant que fVSS fait la répartition des volumes des données cryptées en fonction des tarifs des fournisseurs. Quatrièmement, fVSS améliore la sécurité basée sur le partage de clé secrète en imposant une nouvelle contrainte : aucun groupe de fournisseurs de service ne peut contenir suffisamment de volume de données cryptées pour reconstruire ou casser le secret. Et cinquièmement, fVSS permet l'actualisation de l'entrepôt de données, même si certains fournisseurs de services sont défaillants. Pour évaluer l'efficacité de bpVSS et fVSS, nous étudions théoriquement les facteurs qui influent sur nos approches en matière de sécurité, de complexité et de coût financier dans le modèle de paiement à la demande. Nous validons également expérimentalement la pertinence de nos approches avec le Benchmark schéma en étoile afin de démontrer son efficacité par rapport aux méthodes existantes.Cloud business intelligence is an increasingly popular solution to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. Many security issues are raised by data storage in a public cloud, including data privacy, data availability, data integrity, data backup and recovery, and data transfer safety. Moreover, security risks may come from both cloud service providers and intruders, while cloud data warehouses should be both highly protected and effectively refreshed and analyzed through on-line analysis processing. Hence, users seek secure data warehouses at the lowest possible storage and access costs within the pay-as-you-go paradigm.In this thesis, we propose two novel approaches for securing cloud data warehouses by base-p verifiable secret sharing (bpVSS) and flexible verifiable secret sharing (fVSS), respectively. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. bpVSS and fVSS address five shortcomings in existing secret sharing-based approaches. First, they allow on-line analysis processing. Second, they enforce data integrity with the help of both inner and outer signatures. Third, they help users minimize the cost of cloud warehousing by limiting global share volume. Moreover, fVSS balances the load among service providers with respect to their pricing policies. Fourth, fVSS improves secret sharing security by imposing a new constraint: no cloud service provide group can hold enough shares to reconstruct or break the secret. Five, fVSS allows refreshing the data warehouse even when some service providers fail. To evaluate bpVSS' and fVSS' efficiency, we theoretically study the factors that impact our approaches with respect to security, complexity and monetary cost in the pay-as-you-go paradigm. Moreover, we also validate the relevance of our approaches experimentally with the Star Schema Benchmark and demonstrate its superiority to related, existing methods

Secret Sharing for Cloud Data Security

International audienceCloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm

Sharing-based Privacy and Availability of Cloud Data Warehouses

National audienceCloud computing can help reduce costs, increase business agility and deploy applications with a high return on investment such as data warehouses. However, storing and managing data in the cloud may not be fully trustworthy. In this article, we focus on both data security (data privacy, availability and integrity) and data analysis in the cloud. To solve the data security issue, we propose a new (m,n,t) multi secret sharing scheme based on block cryptography, secret sharing and hash functions. Moreover, we apply this solution onto a cloud data warehouse such that data security and data analysis are addressed. An extensive security and performance analysis shows that the proposed schemes can prevent most attacks, guarantee data availability and integrity, and allow analyzing data at low costs (data storage, data transfer and time computation) in the pay-as-you-go economic model in the cloud

Sharing-based Privacy and Availability of Cloud Data Warehouses

National audienceCloud computing can help reduce costs, increase business agility and deploy applications with a high return on investment such as data warehouses. However, storing and managing data in the cloud may not be fully trustworthy. In this article, we focus on both data security (data privacy, availability and integrity) and data analysis in the cloud. To solve the data security issue, we propose a new (m,n,t) multi secret sharing scheme based on block cryptography, secret sharing and hash functions. Moreover, we apply this solution onto a cloud data warehouse such that data security and data analysis are addressed. An extensive security and performance analysis shows that the proposed schemes can prevent most attacks, guarantee data availability and integrity, and allow analyzing data at low costs (data storage, data transfer and time computation) in the pay-as-you-go economic model in the cloud

A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

International audienceCloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related methods

JINDEX: JSON and index search system for plant germplasm database

To facilitate the development of new varieties and conserve plant germplasm data for future needs, Thailand organizations have developed plant germplasm search systems that provide access and exchange plant genetic resource data like the international organizations. However, the development of plant germplasm search systems using traditional data warehousing creates a limitation in terms of structural flexibility and scalability, as well as search performance. Accordingly, this paper proposes a JSON and index search system for the plant germplasm database named JINDEX. JINDEX is a new approach for implementing a galaxy schema in NoSQL based a hybrid key-value/document data model. Plant germplasm data is stored in JSON files as a document data model, providing a flexible structure of genetic resources for various plants. The plant germplasm data and index files are organized as an on-disk tree structure, which can be accessed regarding a key-value data model to retrieve plant germplasm data efficiently. Experimental results showed that the JINDEX-based plant germplasm search system outperforms the previous plant germplasm search system implemented in a relational database in terms of query response time. Additionally, this JINDEX-based plant germplasm search system has been deployed since 2020, demonstrating that it can work practically

ระบบเฝ้าสังเกตและวิเคราะห์การเจริญเติบโตของอ้อยโดยใช้โดรนสังเกตการณ์Monitoring and Analysis System of Sugarcane Growth Using Observation Drone

อ้อยจัดเป็นพืชเศรษฐกิจที่สำคัญชนิดหนึ่งของประเทศไทย และสามารถนำไปใช้เป็นวัตถุดิบสำหรับอุตสาหกรรมน้ำตาล การสำรวจแปลงอ้อยจะทำให้ทราบถึงการเปลี่ยนแปลงของต้นอ้อยในแปลงอ้อย ดังนั้นจึงมีการใช้โดรนสังเกตการณ์ติดกล้องเพื่อทำการสำรวจแปลงอ้อยในบริเวณที่เข้าถึงได้ยาก งานวิจัยนี้พัฒนาขึ้นเพื่อถ่ายภาพและวิเคราะห์ภาพของแปลงอ้อยที่ครอบคลุมพื้นที่กว้างซึ่งการสังเกตด้วยตาเปล่าอาจทำได้ยากและไม่ทั่วถึง ระบบที่พัฒนาขึ้นประกอบด้วยสองส่วนคือส่วนหน้าและส่วนหลัง สำหรับส่วนหน้าจะเป็นเว็บแอปพลิเคชันบนสมาร์ทโฟนสำหรับเป็นส่วนต่อประสานกับผู้ใช้ ซึ่งใช้ในการรับภาพถ่ายจากโดรนและแสดงผลการวิเคราะห์การประมวลผลภาพ โดยจะรับส่งข้อมูลและภาพกับส่วนหลังผ่านทาง Firebase Realtime Database และ Firebase Cloud Storage ในส่วนหลังจะประกอบด้วยโปรแกรมคำนวณโดยใช้ MATLAB บนเครื่องเซิร์ฟเวอร์และที่เก็บข้อมูลบนคลาวด์ของ Firebase โมเดลสี HSV และ YCbCr รวมทั้งอัลกอริทึม Otsu Thresholding ถูกใช้สำหรับการประมวลผลภาพดิจิทัล จากนั้นใช้สคริปต์ Isgreen สำหรับการแยกสีเพื่อคำนวณเปอร์เซ็นต์ของสีเขียวของภาพแปลงอ้อย ผลลัพธ์ที่ได้จะแสดงเป็นกราฟบนหน้าเว็บแอปพลิเคชัน ซึ่งผลการวิเคราะห์นี้จะช่วยให้ผู้ใช้หรือเกษตรกรสามารถตัดสินใจเกี่ยวกับเวลาที่เหมาะสมที่จะเก็บเกี่ยวต้นอ้อยได้ จากการทดสอบการทำงานของเว็บแอปพลิเคชันพบว่าสามารถทำงานได้อย่างถูกต้องคิดเป็นร้อยละ 98.46 และจากการทดสอบการประมวลผลภาพผ่านโปรแกรม MATLAB โดยใช้ชุดข้อมูลปกติและไม่ปกติ (โดยการหมุนภาพ) พบว่าโปรแกรมสามารถแยกชนิดของพืชได้อย่างถูกต้องคิดเป็นร้อยละ 98.89 และ 93.85 ตามลำดับSugarcane is one of the important economic crops of Thailand and generally used as a raw material for sugar industry. The survey of sugarcane field indicates the changes of sugarcane plants in sugarcane field. Therefore, the observation drone with camera is used for survey sugarcane field in hard-to-reach areas. This research is developed to take and analyze images of sugarcane field covering a wide area that cannot be observed thoroughly. The developed system consists of two parts: front-end part and backend part. The front-end part contains a web application on smartphone for user interface which is used to take the images from drone and show the results of image processing analysis. Data and images are transferred to the back-end part via Firebase Realtime Database and Firebase Cloud Storage. The back-end part includes a computational program using MATLAB on server and data storage on Firebase Cloud. HSV and YCbCr color models and also Otsu Thresholding algorithm are used for digital image processing. Then Isgreen script is used for color separation to calculate the percentage of a green color of sugarcane field images. The results are displayed in graph on the web application. These help users or farmers make a decision about the right time for harvesting sugarcane. From the functional test of the web application, it was found that 98.46% of the test was correct. Moreover, based on the image processing test via MATLAB using normal and non-normal data sets (by rotating the images), it was found that the program was able to correctly distinguish 98.89% and 93.85% of the plant species respectively