1. Data Confidentiality on Clouds Sharad Mehrotra University of California, Irvine

2. Cloud Computing X as a service, where X is: X as a service, where X is: –Infrastructure, platforms, Software, – Storage, Application, test environments… Characteristics: Characteristics: –Elastic-- Use as much as your needs –Pay for only what you use –Don’t worry about: –system management headaches –Failures –loss of data due to failures –.. –Cheaper due to economy of scale –Better control over IT investments Challenges Challenges –scalability, elasticity, consistency, big data management, interoperability, migration, multi-tenancy, pricing … 2 Utility model

3. Cloud Computing X as a service, where X is: X as a service, where X is: –Infrastructure, platforms, Software, – Storage, Application, test environments… Characteristics: Characteristics: –Elastic -- Use as much as your needs –Pay for only what you use –Don’t worry about –No system management headaches –, loss of data due to failures –Cheaper due to economy of scale –Better control over IT investment Infrastructure Challenges: Infrastructure Challenges: –Scale, multi-tenancy, elasticity, consistency, big data management, interoperability, migration, pricing … 3 Utility model

4. Implications of Loss of Control 4 End Users Cloud Integrity Integrity Will the CSP serve my data correctly? Will the CSP serve my data correctly? Can my data get corrupted? Can my data get corrupted? Availability Availability Will I have access to my data and services at all times? Will I have access to my data and services at all times? Security Security Will the CSP implement its own security policies appropriately? Will the CSP implement its own security policies appropriately? Privacy & confidentiality Privacy & confidentiality Will sensitive data remain confidential? Will sensitive data remain confidential? Will my data be vulnerable to misuse? By other tenants? By the service provider? Will my data be vulnerable to misuse? By other tenants? By the service provider?

5. So will Crypto Researchers Solve the Problem? 5 Large body of research in applied crypto over 2 decades Generality, Efficiency, Security Binary notion of security Semantic security, Perfect Secrecy Great for some user- communities (military, government, trade-secrets) Overprotection if user- community is common users of the cloud. -How much are we willing to pay to prevent leakage of “Mom’s secret recipe”. -.-. Encrypte d search / computati on Queries over encrypted (semi- )structure d data Bucketizatio n (Hacigumus, Sigmod 2002, Hore, VLDB 2004, VLDBJ 2012) OPE (Agraw al Sigmod 2004) Range queries on encrypted data (Shi, S&P 2007) Onion encryption (Popa et al., Sosp ‘11) Fully homomorph ic encryption (Gentry, STOC 2009) Keyword search over encrypted text Symmetri c key based schemes Searchable document encryption (Song, S&P 2000) Encrypted bloom filters (Goh, 2003) Encrypted inverted lists (Curtmola, CCS 2006) Public- key based schemes Bilinear maps (Boneh,Eu rocrypt 2003) Conjuncti ve keyword search (Golle, ACNS 2004) Other schemes (informati on hiding) Coloring based document indexing (Hore, SDM 2012) Classification of Research on Encrypted Search [ Hacigumus, et. al. Survey, 2007, Bagherzandi et al., Encyclopedia entry 2011 ]

6. Risk Based Data Processing in Clouds Risk Based Approach Data (R) Workload (Q) Sensitivity Disclosure Performance Cost Usability Each point represents a different representation of data User Specific constraints on disclosure, costs, etc. Multi Criteria Optimization Data, Workload Partitions (R Cli, R Serv, Q Cli, Q Serv ) and Workload Execution Plan Challenges: Modeling risks – function of trust, security, data representation, sensitivity, exposure duration, usefulness to adversary, … Modeling risks – function of trust, security, data representation, sensitivity, exposure duration, usefulness to adversary, … Mechanism to trace “sensitivity/risk provenance” Mechanism to trace “sensitivity/risk provenance” Mechanisms to Partition Computation & data -- Robust, adaptive, efficient, general,.. Mechanisms to Partition Computation & data -- Robust, adaptive, efficient, general,.. Systems we are building (RADICLE Project at UCI) CloudProtect – (usability versus confidentiality tradeoff) CloudProtect – (usability versus confidentiality tradeoff) –empowers end-users to control loss of data in using web applications such as Box, Google Drive, picasa, shutterfly, etc. Hybridizer – (Cost, performance, confidentiality tradeoffs) Hybridizer – (Cost, performance, confidentiality tradeoffs) – partitioning Hive & map reduce jobs across hybrid clouds to control information leakage Empower owners to strike a balance between risk, performance, and costs by steering data & computation appropriately in mixed trust environments