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Security and Privacy in Cloud Computing

Security and Privacy in Cloud Computing. Ragib Hasan University of Alabama at Birmingham CS 491/691/791 Fall 2011. Lecture 16 10 / 11 / 2011. Announcements. No class on Thursday due to Fall Break Homework for this week will be due by 10/12. Announcements. Project progress meetings:

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Security and Privacy in Cloud Computing

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  1. Security and Privacy in Cloud Computing Ragib HasanUniversity of Alabama at BirminghamCS 491/691/791 Fall 2011 Lecture 16 10/11/2011

  2. Announcements No class on Thursday due to Fall Break Homework for this week will be due by 10/12 cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  3. Announcements Project progress meetings: • Starting from next week, we’ll have weekly progress meetings with groups during office hours • Talk to me next week to set up meeting schedule cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  4. Enforcing Data Privacy in Cloud Goal: Examine techniques for ensuring data privacy in computations outsourced to a cloud Review Assignment #8: (Due 10/12) Roy et al., Airavat: Security and Privacy for MapReduce, NSDI 2010 cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  5. What does privacy mean? Information Privacy is the interest an individual has in controlling, or at least significantly influencing, the handling of data about themselves. Confidentiality is the legal duty of individuals who come into the possession of information about others, especially in the course of particular kinds of relationships with them. cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  6. Problem of making large datasets public Model: • One party owns the dataset • Another party wants to run some computations on it • A third party may take data from the first party, run functions (from the second party) on the data, and provide the results to the second party Problem: • How can the data provider ensure the confidentiality and privacy of their sensitive data? cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  7. Problem of making large datasets public • Massachusetts Insurance Database • DB was anonymized, with only birthdate, sex, and zip code made available to public • Latanya Sweeny of CMU took the DB and voter records, and pinpointed the MA Governor’s record • Netflix Prize Database • DB was anonymized, with user names replaced with random IDs • Narayanan et al. used Netflix DB and imDB data to de-anonymize users cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  8. Differential Privacy schemes can ensure privacy of statistical queries Differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its records. Informally, given the output of a computation or a query, an attacker cannot tell whether any particular value was in the input data set. cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  9. Securing MapReduce for Privacy and Confidentiality • Paper: • Roy et al., Airavat: Security and Privacy for MapReduce • Goal: Secure MapReduce to provide confidentiality and privacy assurances for sensitive data cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  10. System Model Data providers: own data sets Computation provider: provides MapReduce code Airavat Framework: Cloud provider where the MapReduce code is run on uploaded data cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  11. Threat Model • Assets: Sensitive data or outputs • Attacker model: • Cloud provider (where Airavat is Run) is trustworthy • Computation provider (user who queries, provides Mapper and Reducer functions) can be malicious • Functions provided by the Computation provider can be malicious. • Cloud provider does not perform code analysis on user-generated functions • Data provider is trustworthy cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  12. MapReduce MapReduce is a widely used and deployed distributed computation model Input data is divided into chunks Mapper nodes run a mapping function on a chunk and output a set of <key, value> pairs Reducer nodes combine values related to a particular key based on a function, and output to a file cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  13. Key design concepts Goal: Ensure privacy of source data Concept used: Differential privacy – ensure that no sensitive data is leaked. Method used: Adds random Laplacian noise to outputs cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  14. Key design concepts • Goal: Prevent malicious users from preparing sensitive functions that leak data. • Concept used: Functional sensitivity - How much the output changes when a single element is included/removed from inputs • More sensitivity: more information is leaked • How is used? : • Airavat requires CPs to give range of possible output values. • This is used to determine sensitivity of CP-written mapper functions. cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  15. Key design concepts • Goal: Prevent users from sending many brute force queries and try to reveal the input data. • Concept used: Privacy budget (defined by data provider) • How used: • Data sources set privacy budget for data. • Each time a query is run, the budget is decreased, and • Once the budget is used up, user cannot run more queries. cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  16. Airavat system design • Mappers are provided by computation provider, and hence are not trusted • Reducers are provided by Airavat. They are trusted • Airavat only supports a small set of reducers. • Keys must be pre-declared by CP (why?) • Airavat generates enough noise to assure differential privacy of values • Range enforcers ensure that output values from mappers lie within declared range cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  17. Security via Mandatory Access Control In MAC, Operating System enforces access control at each access Access control rights cannot be overridden by users Airavat uses SELinux – a special Linux distribution that supports MAC (developed by NSA) cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  18. Security via MAC Each data object and process is tagged showing the trust level of the object Data providers can set a declassify bit for their data, in which case the result will be released when there is no differential privacy violation cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  19. Implementation Airavat was implemented on Hadoop and Hadoop FS. cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

  20. Further reading Cynthia Dwork defines Differential Privacy, interesting blog post that gives high level view of differential privacy. http://www.ethanzuckerman.com/blog/2010/09/29/cynthia-dwork-defines-differential-privacy/ cs491/691/791 Fall 2011 Lecture 16 | UAB | Ragib Hasan

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