security and privacy in cloud computing

Ragib HasanJohns Hopkins Universityen.600.412 Spring 2011

Lecture 804/04/2011

Security and Privacy in Cloud Computing

en.600.412 Spring 2011 Lecture 8 | JHU | Ragib Hasan

Enforcing Data Privacy in Cloud

Goal: Examine techniques for ensuring data privacy in computations outsourced to a cloud

Review Assignment #7: (Due 4/11)Roy et al., Airavat: Security and Privacy for MapReduce, NSDI 2010

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Recap: Cloud Forensics (Bread & Butter paper from ASIACCS 2010)

• Strengths?

• Weaknesses?

• Ideas?

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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.

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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?

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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

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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.

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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

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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

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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

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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

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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

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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.

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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.

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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

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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)

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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

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Implementation

• Airavat was implemented on Hadoop and Hadoop FS.

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Further reading

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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/





security and privacy in cloud computing

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information privacy

cloud provider

privacy assurances

privacy mean

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data setscomputation

jhu ragib hasanrecap

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