Presentation given at the SWIM Seminar (University of Tsukuba) about MONOMI*. This presentation is based on the uploader's understanding of the paper and may contain inaccurate interpretations. A summary of the paper is available at: https://mshcruz.wordpress.com/2016/07/01/summary-monomi/ *Tu et al.: "Processing Analytical Queries over Encrypted Data". VLDB 2013.

1. Processing Analytical Queries
over Encrypted Data
Stephen Tu, M. Frank Kaashoek,
Samuel Madden, and Nickolai Zeldovich
39th International Conference on Very Large Data Bases
Riva del Garda, Trento, Italy, August 2013
SWIM Seminar
May 19th, 2015
Mateus Cruz

2. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
2 / 31

3. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
3 / 31

4. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OVERVIEW
System called MONOMI
Extension of CryptDB
Analytical queries over encrypted data
Data protected against server compromises
Modest overhead
Slowdown of 1.03 to 2.33×
4 / 31

5. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
MAIN IDEAS
Split client/server execution
Optimization techniques
Per-row precomputation
Space-efficient encryption
Grouped homomorphic addition
Pre-filtering
Designer
Physical data layout
Planner
Efficient execution plan for queries
5 / 31

6. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
USED ENCRYPTION SCHEMES
6 / 31

7. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
ARCHITECTURE
7 / 31

8. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
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9. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
INTUITION
The server cannot execute all queries
Limitations of cryptosystems
Cannot transfer all data to the client
Large amount of data
Divide execution
Execute as much as possible on server
Transfer data to the client when it is not
possible to execute on the server
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10. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
LIMITATIONS OF SERVER EXECUTION
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty) AS value
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty) * 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
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11. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
LIMITATIONS OF SERVER EXECUTION
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty) AS value
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM
Addition and comparison in-
volve incompatible encryption
schemes
(ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty) * 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
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12. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
LIMITATIONS OF SERVER EXECUTION
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty) AS value
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
No efficient encryption
scheme allows multiplica-
tion of two encrypted values
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
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13. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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14. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET
Precomputed
multiplication
)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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15. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1
Reference to
the columns
of the child
operator
]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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16. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
Deterministic
encryption of the
value :1
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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17. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP
Concatenation of all values
from each GROUP BY group
(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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18. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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19. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
Outmost SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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20. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
Innermost SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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21. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
Decrypts the data
at the client
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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22. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs:
Multiplication
by constant
[sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
11 / 31

23. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter:
Filter referring to
the HAVING clause
sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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24. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
key:[$1]
LocalProjection
Selection of ps partkey and
of the summation of the pre-
computed expression
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
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25. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPLIT QUERY PLAN
Example (TPC-H Q11)
SELECT ps partkey,
SUM (ps supplycost * ps availqty)
AS VALUE
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1
GROUP BY ps partkey
HAVING SUM (ps supplycost * ps availqty) > (
SELECT SUM (ps supplycost * ps availqty)
* 0.0001
FROM partsupp JOIN supplier JOIN nation
WHERE n name = :1 )
ORDER BY value DESC;
LocalSort
Sorting referring to the
ORDER BY clause
key:[$1]
LocalProjection
exprs: [$0, sum($1)]
LocalGroupFilter
filter: sum($1) > subquery0()
LocalDecrypt
pos: [$0, $1]
LocalProjection
exprs: [sum($0) * 0.0001]
RemoteSQL
SELECT
ps parkey DET,
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
GROUP BY ps partkey DET
LocalDecrypt
pos: [$0]
RemoteSQL
SELECT
GROUP(precomp DET)
FROM ... WHERE
n name DET = 0xabcdef
11 / 31

26. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
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27. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
PER-ROW PRECOMPUTATION
Prior computation of certain expressions
Materialized using additional columns
Decision made by the designer module
Example
SUM (ps supplycost * ps availqty)
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28. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPACE-EFFICIENT ENCRYPTION
Minimize ciphertext expansion
FFX mode of operation: n bits to n bits
Pack multiple columns in a row
Pack multiple rows into a single Paillier
Packed ciphertexts are kept in separate files
on the local file system
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29. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
GROUPED HOMOMORPHIC ADDITION
Packed aggregates computed with a single
modular multiplication
(a1||...||an) + (b1||...||bn) = (a1 + b1)||...||(an + bn)
E(a1||...||an)×E(b1||...||bn) = E((a1+b1)||...||(an+bn))
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30. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
PRE-FILTERING
Minimize data sent to the client
Apply filtering to encrypted data
Example
SELECT l orderkey FROM lineitem
GROUP BY l orderkey
HAVING SUM(l quantity) > :1
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31. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
PRE-FILTERING
Minimize data sent to the client
Apply filtering to encrypted data
Example
SELECT l orderkey FROM lineitem
GROUP BY l orderkey
HAVING SUM(l quantity) > :1
Incompatible schemes for SUM
and comparison (>)
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32. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
PRE-FILTERING
Minimize data sent to the client
Apply filtering to encrypted data
Example
SELECT l orderkey det,
PAILLIER SUM(l quantity paillier)
FROM lineitem
GROUP BY l orderkey det
HAVING MAX(l quantity ope) > encrypt ope(m)
OR COUNT(*) > (:1 / m)
16 / 31

33. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
PRE-FILTERING
Minimize data sent to the client
Apply filtering to encrypted data
Example
SELECT l orderkey det,
PAILLIER SUM(l quantity paillier)
FROM lineitem
GROUP BY l orderkey det
HAVING MAX(l quantity ope) > encrypt ope(m
Maximum value
of the column
l quantity
)
OR COUNT(*) > (:1 / m)
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34. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
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35. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
INTUITION
Optimizations are not always better
Designer
Best physical design
– Encryption schemes
– Precomputed expressions
Planner
Best query plan at runtime
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36. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
DESIGNER: INPUT AND OUTPUT
Input
Representative query workload
– Q0, Q1, ..., Qn
– Chosen by the administrator
Sample data
Space constraint factor (optional)
Output
Physical design of the server
– Set of encrypted columns to materialize
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37. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
DESIGNER: ALGORITHM
1 Consider all operations in query Qi
Check what expression would allow execution
on the server
EncSeti
– Set of value, scheme pairs for Qi
Example
WHERE x = :1 generates a x, DET pair,
referring to the x column
ORDER BY x + y generates a x + y, OPE
pair, referring to a precomputed x + y value
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38. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
DESIGNER: ALGORITHM
2 The designer invokes the planner to
determine the best way to execute Qi
The planner computes PowSeti
– Contains the subsets of EncSeti
The planner constructs an execution plan for Qi
for each element of the power set
3 The planner uses a cost model to estimate
the fastest execution plan
20 / 31

39. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
COST MODEL
Sum of three components
Execution time on the server
Data transfer time
Post-processing on client (decryption)
Constraints are considered using an Integer
Linear Programming (ILP) formulation
21 / 31

40. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
22 / 31

41. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
IMPLEMENTATION
8.000 lines of Scala for designer/planner
4.000 lines of C++ for client library
OpenSSL for cryptography
Each table is mapped to an encrypted table
Copies of columns (different cryptosystems)
Do not support
Views
Pattern matching with two or more patterns
Example
LIKE ’%foo%bar%’
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42. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
ENVIRONMENT
Client
Four 4-core 2.2GHz Intel Xeon E5520
24GB RAM
Server
Four 4-core 2.4GHz Intel Xeon E5530
24GB RAM
Multiple cores used for decryption
Postgres 8.4
Memory limit: 8GB
TPC-H scale 10 dataset
24 / 31

43. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OVERALL EFFICIENCY
Median overhead of 1.24×
25 / 31

44. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
TECHNIQUE PERFORMANCE
Cumulative use of optimization techniques
26 / 31

45. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SPACE OVERHEAD
27 / 31

46. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SENSITIVITY TO DESIGNER INPUT
Choosing representative queries
Aggregation over expressions
Expressions requiring precomputation
Very selective WHERE on large relations
28 / 31

47. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
SECURITY
Plaintext is never revealed
OPE is used infrequently
Leaks order
29 / 31

48. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
OUTLINE
1 Introduction
2 Split Execution
3 Optimization Techniques
4 Designer and Planner
5 Experiments
6 Conclusion
30 / 31

49. Introduction Split Execution Optimization Techniques Designer and Planner Experiments Conclusion
CONCLUSION
Novel system: MONOMI
Analytic queries over confidential data
New optimization techniques
Use of designer and planner
Modest overheads
Execution: 1.24×
Space: 1.72×
31 / 31