Searchable Encryption Systems Christopher M. FrenzJuly 2012
The Current State of InformationInsecurity July 2012 - Yahoo confirmed that over 400,000 user name and password combinations were stolen June 2012 - LinkedIn suffered a data breach that resulted in the theft of over 6 million unsalted user passwords From 2005 to July 2012 there have been 3,226 data breaches that resulted in over 562,872,534 records being compromised (http://www.privacyrights.org/data-breach). Verizon reported that 2011 was the second largest year for data breaches since they started investigating them in 2004
Security Controls Publications, such as NIST Special Publication 800-53 and others like it, list well over 150 possible security controls that could be implemented to improve upon information security This presentation is going to focus on the use of encryption as a security control
Common Uses of Encryption Today Securing Data Transmission ◦ SSL/TLS ◦ IPSEC ◦ S/MIME ◦ Etc Securing Stored Data ◦ TrueCrypt ◦ Bitlocker ◦ Etc
The Growth of Cloud Computing In 2009 cloud computing services were reported to be valued at $17.4 billion with the market expected to grow to over $44 billion by 2013
Cloud Computing Cloud services can offer some security advantages ◦ e.g. - resource pooling to build more robust infrastructures ◦ options for the dynamic scaling of services to help maintain availability But they are not without risk ◦ e.g. – much of your data is being stored by a trusted? 3rd party
Role of Encryption One of the primary ways of ensuring that cloud hosted data remains secret is via the encrypted transmission of data and the encrypted storage of data However, data hosted on a cloud provider’s system cannot be searched without first being decrypted How can this issue be dealt with?
Yao’s Garbled Circuits Yao developed a system whereby one party in the communication (party A) creates a garbled circuit that is capable of computing a desired function in such a way that the inputs required from party A are encoded into the garbled circuit in such a manner that party B cannot determine what the inputs are Party B is able to use his inputs in conjunction with the garbled circuit to compute the answer to the desired function This allows party A and B to retrieve the desired information while at the same time limiting the amount of information disclosure to just the result of the computed function
Limitation of Yao’s Garbled Circuits Yao’s Garbled Circuits only work to prevent “honest but curious” attackers That is attackers that only attempt to run the circuit as designed Increasingly research around such secure communication is focused on the concept of homomorphic encryption
Homomorphism Homomorphism occurs in a cryptosystem when a mathematical operation (i.e multiplication and addition) that is enacted on the cipher text has the same effects on the plain text C = Cipher Text, P = Plain text 5*C=5C 5C decrypted yields 5P
Homomorphic Properties ofCurrent Encryption Systems Symmetric encryption systems like AES and DES are not homomorphic Some asymmetric encryption systems like RSA and ElGammal are partially homomorphic in that they can support one homomorphic math operation
Partially Homomorphic EncryptionSystems Boneh, Goh, and Nissim (BGN) cryptosystem was developed to support an arbitrary number of additions and one multiplication Melchor, Gaborit, and Herranz developed improvements upon BGN which allowed for an arbitrary number of additions and 2 multiplications
Fully Homomorphic Encryption Developed by Craig Gentry in 2009 This fully homomorphic encryption system allows for an arbitrary number of additions and an arbitrary number of multiplications to be performed while still demonstrating the same effects on both the cipher text and plain text
Applications of Fully HomomorphicEncryption Private Information Retrieval without the need to decrypt data Filtering/sorting encrypted emails Improved security of electronic medical records Analysis of electronic medical record data without decrypting the data Secure electronic voting
Limitation – Time Homomorphic encryption is computationally intensive A Google search using homomorphic encryption would require approximately a trillion times as much computing time as a normal Google search Even if Moore’s Law continues to hold true, it will be at least 40 years before homomorphic encryption based search resembles the search speeds of today
Addressing this limitation GPGPU – Performing these operations on a GPU instead of a CPU can improve performance ◦ A CUDA implementation of the PIR algorithms proposed by Aguilar and Gaborit was used to demonstrate data processing rates of up to 2Gbits/sec FPGAs – performing these operations on specialty hardware can improve performance
Limitation – Security? These algorithms are still in their infancy They are not yet as well tested and vetted by the cryptographic community as other encryption algorithms There may be security flaws in the algorithms that have not yet been identified
Conclusion Homomorphic encryptions holds great promise for the future There are limitations with these algorithms, but with continued research these limitations could be reduced The ability to search and analyze encrypted data sets will likely create many novel applications that make use of homomorphic encryption systems
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