searchable encryption systems
DESCRIPTION
A brief introduction to the concepts and promise surrounding searchable encryption systems.TRANSCRIPT
Searchable Encryption Systems Christopher M. Frenz
July 2012
The Current State of Information
Insecurity 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 of
Current 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 Encryption
Systems 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 Homomorphic
Encryption 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