Ragib Hasan Johns Hopkins University en Spring 2011 Lecture 8 04/04/2011 Security and Privacy in Cloud Computing

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 /4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

Recap: Cloud Forensics (Bread & Butter paper from ASIACCS 2010) Strengths? Weaknesses? Ideas? 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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. 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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? 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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. 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 pairs Reducer nodes combine values related to a particular key based on a function, and output to a file 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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. 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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. 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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) 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

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 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

Implementation Airavat was implemented on Hadoop and Hadoop FS. 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan

Further reading 4/4/2011en Spring 2011 Lecture 8 | JHU | Ragib Hasan Cynthia Dwork defines Differential Privacy, interesting blog post that gives high level view of differential privacy. differential-privacy/