talk benjamin nguyen
Post on 23-Jun-2015
75 Views
Preview:
TRANSCRIPT
PR SMPRiSM Lab. - UMR 8144
Privacy Preserving SQL Query Execution on Distributed Data
Quoc-Cuong To, Benjamin Nguyen, Philippe PucheralSMIS Project
LaHDAK SeminarOrsay4th March 2014
Université de Versailles et St-Quentin INRIA RocquencourtCNRS
PR SMPRiSM Lab. - UMR 8144
PART I The New Oil
I. The New OilII. Trusted CellsIII. Global SQL QueriesIV. Cost Model and
ExperimentsV. Conclusion
PR SM 3
Mass-generation of (personal) data
Data sources have mostly turned digital
Analog processese.g., photography, films
Paper-based interactions e.g., banking, e-administration
Communicationse.g., email, SMS, MMS, Skype
Where is your personal data? … In data centers
112 new emails per day Mail servers
65 SMS sent per day Telcos
800 pages of social data Social networks
Web searches, list of purchases google, amazon
People recording
People listnening
St Peter's Place, Roma
WHY ?Is this a problem ?
/ 41
Everything is free…
Information Extraction
PR SM 4
Personal data is the new oil
Is this good news ?
$2 billion a year spend by US companies
on third-party data about individuals
(Forrester Report)
$44.25 is the estimated return on $1
invested in email marketing (oil is up to 0.5$/yr)
High Market Value Companies Facebook: value / #accounts 50$ Google: $38 billion business sells ads based on how people search the Web Amazon (knows purchase intent), mail order systems companies (gmail), loyalty
programs (supermarkets), banks & insurrance, employement market (linkedIn,
viadeo), travel & transportation (voyages-sncf), the « love » market (meetic), etc.
/41
PR SM 5
Personal data is the new oil
How would oil companies behave ?
• Exploit your oil field for free Know all about you
• Offer “extra” services Refine their knowledge
• Provide real services to their paying customers
(e.g. advertisement and profiling, location tracking and spying, …)
In other words : your personal data would be
processed by sophisticated data refineries…
REGARDLESS OF YOUR PRIVACY !
It’s the business model…
… or bad news ?
/41
Their choice
Your choice
PR SM 6
Is the current centralised model good wrt privacy protection?
Intrinsic problem #1: personal data is exposed to sophisticated attacks
–High benefits to successful hack
–One person negligence may affect millions
Intrinsic problem #2: personal data is hostage of sudden privacy changes
–Centralised administration of data means delegation of control
–This leads to regular changes, with application (and business)
evolution, with mergers and acquisition, etc. (e.g Facebook 2012)
Increasing security is only a partial solution since does not solve those
intrinsic limitations
E.g., TrustedDB [BS12] proposes tamper-resistant hardware to secure
outsourced centralized databases.
/41
PR SM 7
A New Hope
A Personal Data Ecosystem…
… built around user-centricity and trust,
achieved through a decentralized architecture
7
THE TRUSTED CELL !
I want my privacy back !!
/41
Our goals : Preserve current USER functionalities Hinder uncontrolled data exploitation & privacy violations
Our targets : General Data Management Applications : SQL “Low cost” solutions (i.e. acceptable by general public)
PR SMPRiSM Lab. - UMR 8144
PART II
Trusted Cells
I. The New OilII. Trusted CellsIII. Global SQL QueriesIV. Cost Model and
ExperimentsV. Conclusion
PR SM 9
The Secure (Trusted) Personal Data Server Approach [AAB+10]
9
Personal database is• Well-organized• Tamper resistant• Controlled by the owner
(sharing, retention, audit)• Accessible in disconnected
mode
Approach characteristics :
• Based on tamper-resistant HW
• Well Structured World (R-DB, limited apps)
• Uniform equipment
TRUSTED CELL
/41
PR SM 10
Why trust personal secure HW solutions?
1. Users store their own data
minimize abusive usage
2. Auto-administered platform
no DBA attack (even by user)
3. Enforce privacy principles for externalized (shared) data
best if the recipient of the data is another TC
4. Tamper-resistance + certified code/secure execution + single user + physical access needed
ratio cost/benefit of an attack is very high
Tam
per
resi
stan
ce
Gemalto secure token
SMIS token (ZED)
Trust Zone architecture
Dedicated HW device
PC ? (social trust / open
source)
/41
PR SM 11
The Trusted Cell Asymmetric Architecture
Durability,Availability
Secure Computation
Export Data
11
TC asymmetric architectureBuilt using Secure Portable Tokens as Trusted Cells (called here Trusted Data Server or TDS) /
Cloud as Supporting Server Infrastructure (SSI).Challenges :
Local (Embedded) data management (not my work : Anciaux, Bouganim, Pucheral et al.)Global querying (Part III)Data export management (MinExp Project with CG78 & LIX)
HIGH POWER / AVAILABILITYLOW / NO TRUST
LOW POWER / AVAILABILITYHIGH TRUST
ASYMMETRIC
Encrypted
Private Data Generated (e.g. sensor)
/41
PR SMPRiSM Lab. - UMR 8144
PART IIIGlobal SQL Queries on the Asymmetric Architecture
I. The New OilII. Trusted CellsIII. Global SQL QueriesIV. Cost Model and
ExperimentsV. Conclusion
PR SM 13
Example Trusted Cell : a Trusted Data Server (TDS)
Token Characteristics :
• High security:• High ratio Cost/Benefit of an attack;• Secure against its owner;
• Modest computing resources (~10Kb of RAM, 50MHz CPU);
• Low availability: physically controlled by its owner; connects and disconnects at it will
13
How to compute global queries over decentralized personal data stores while respecting users’ privacy?
AuthorizedQuerier
Average Salary in Orsay
Unauthorized Querier
PR SM 14
TC can be : Unbreakable (honest)Broken (Weakly Malicious)
Infrastructure (SSI) can be :
Honest but curious (Semi-honest)
Weakly-Malicious (Covert Adversary = does not want to be detected)
Secure Global Computation on TCs
PROBLEM : How to perform global queries on the asymmetric
architecture? (i.e. using data from many/all cells)
The « classical » problem of Secure Global Computation (e.g SMC) is more general and makes no trust assumption.
THREAT MODEL :THREAT MODEL :
/41
HBC + Unbreakable “simple protocols” presented here (EDBT’14 [TNP14])WM + Broken Must be prevented ! (via security primitives) see [ANP13]
PR SM 15
Is this a new problem ?
Several approaches are possible to securely perform global computations:
1. Use only an untrusted server/cloud/P2P and use generic (and costly)
algorithms. (e.g. Secure Multi-Party Computing [Yao82, GMW87, CKL06], fully
homomorphic encryption [Gent09]) Problem = COST
2. Use only an untrusted server/cloud/P2P and develop a specific algorithm for
each specific class of queries or applications. (e.g. DataMining Toolkit [CKV+02])
Problem = GENERICITY
3. Introduce a tangible element of trust, through the use of a trusted
component and develop a generic methodology to execute any centralized
algorithm in this context. ([Katz07, GIS+10, AAB+10]) Problem = TRUST
/41
PR SM 16
Hypothesis on Querier and SSI
Querier:• Shares the secret key with TDSs (for encrypt the query & decrypt
result).
• Classical Access control policy (e.g. RBAC):– Cannot get the raw data stored in TDSs (get only the final result)
– Can obtain only authorized views of the dataset ( do not care about inferential attacks)
Supporting Server Infrastructure:• Doesn’t know query (so, attributes in GROUP BY clause) b/c query is
encrypted by Querier before sending to SSI.
• Has prior knowledge about data distribution.
• Honest-but-curious attacker: Frequency-based attack– SSI matches the plaintext and ciphertext of the same frequency.
e.g. investigates remarkable (very high/low) frequencies in dataset distribution
(e.g., X is the only person with a given (high) age and still working and earning money → if I
find a group with only one member I can deduct that X participates in the dataset). 16
PR SM 17
Solution Overview
171) Query
Supporting ServerInfrastructure (SSI)
…
SELECT <attribute(s) and/or aggregate function(s)>FROM <Table(s) / SPTs>[WHERE <condition(s)>][GROUP BY <grouping attribute(s)>][HAVING <grouping condition(s)>][SIZE <size condition(s)>];
2) Collection andFiltering phase
3) Aggregation phase
Stop condition: max #tuples or max time
John, 35K Mary, 43K Paul, 100K
SELECT age, AVG(salary)FROM userWHERE town = “Orsay”GROUP BY ageHAVING MIN(salary) > 0SIZE
4) Aggregate Filtering phase
PR SM 18
Proposed Solutions
The main difficulty is with AGGREGATE QUERIES !!
Solutions vary depending on which kind of encryption is used, how
the SSI constructs the partitions, and what information is revealed to
the SSI.
• Secure aggregation solution
• Noise-based solutions– random (white) noise
– noise controlled by the complementary domain
• Histogram-based solutions
We investigate these solutions along the directions of
performance and security. 18
PR SM 19
Secure Aggregation
19
Supporting ServerInfrastructure (SSI)
…
encrypts its data using non-deterministic encryption
Form partitions (fit resource of a TDS)
Hold partial aggregation (Gij,AGGk)
Querier
}
(25y, Orsay, 35K)
(#x3Z, aW4r)
(45y, Orsay, 43K) (53y, Paris, 100K)
Q: SELECT Age, AVG(Salary) WHERE city = Orsay GROUP BY Age HAVING Min(Salary) > 0
($f2&, bG?3)
No answer ?
(#x3Z, aW4r)($f2&, bG?3)($&1z, kHa3)
…(T?f2, s5@a)
(#i3Z, afWE)(T?f2, s!@a)($f2&, bGa3)
(#x3Z, aW4r)($f2&, bG?3)($&1z, kHa3)
(?i6Z, af~E)(T?f2, s5@a)(5f2A, bG!3)
(25, 35K)(45, 43K)(45, 37K)
(25, [35K,1])(45, [40K,2])
(F!d2, s7@z)(ZL5=, w2^Z)
Final Agg(#f4R, bZ_a)(Ye”H, fw%g)(@!fg, wZ4#)
(25, 29.5K)(45, 43.7K)…
Evaluate HAVING clause
Final Result(#f4R, bZ_a)(Ye”H, fw%g)
Qi= <EK1(Q),Cred,Size>
Decrypt Qi Check AC rules
Decrypt Qi Check AC rules
Decrypt Qi Check AC rules
PR SM 20
Noise Based Protocols
Secure Aggregation Efficiency problem :nDet_Enc on AG SSI cannot gather tuples belonging to the same group into same partition.
But :Det_Enc on AG frequency-based attack.
Idea : Add noise (fake tuples) to hide distribution of AG.
How many fake tuples (nf) needed? disparity in frequencies among AG – small nf: random noise
– big nf: white noise
– nf = n-1: controlled noise (n: AG domain cardinality)
Efficiency: – Each TDS handles tuples belonging to one group (instead of large partial
aggregation as in SAgg)– However, high cost of generating and processing the very large number of
fake tuples
PR SM 21
Nearly Equi-Depth Histogram Solution
1. Distribution of AG is discovered
and distributed to all TDSs.
2. TDS allocates its tuple to
corresponding bucket.
3. TDS send to SSI:
{h(bucketId),nDet_Enc(tuple)}
Consequences :
21
We do not generate & process too many fake tuples
We do not handle too large partial aggregation
True Distribution Nearly equi-depth histogram
Problem : Distribution must be discovered
This can be done “offline” using secure
aggregation !
PR SM 22
Information Exposure Analysis (DCJP+03)
22
To measure Information Exposure, we consider the probability that an attacker (here the Honnest but Curious SSI) can reconstruct the plaintext table (or part of the table) using
the encrypted table and his prior knowledge about global distributions of plaintext attributes.
Information Exposure is noted :
• n is the number of tuples
• k is the number of attributes
• ICi,j is the value in row i and column j of the inverse cardinality ( = 1/number of plaintext values that could correspond)
• Nj is the number of distinct plaintext values in the global distribution of attribute in column j (i.e., Nj ≤ n).
,1 1
1 kn
i ji j
ICn
PR SM 23
23
_1 1 1
1 11/
k kn
S Agg ji j jj
Nn N
SAgg: ICi,j = 1/Nj for all i,j
•n: the number of tuples, •k: the number of attributes, •ICi,j : IC for row i and column j•Nj: the number of distinct plaintext values in the global distribution of attribute in column j (i.e., Nj ≤ n)
_1
min( ) 1/k
ED Hist jj
N
EDHist: requires finding all possible partitions of the plaintext values such that the sum of their occurrences is the cardinality of the hashed value: NP-Hard multiple subset sum problem
Noise_based & ED_Hist have a uniform distribution of the AG: ɛED_Hist = ɛNoise_based
Plaintext: _1 1
11 1
kn
P Texti jn
ɛS_Agg ≤ ɛED_Hist =ɛNoise_based <1
Information Exposure Analysis (Damiani et al. CCS 2003)
PR SMPRiSM Lab. - UMR 8144
PART IV
Cost Model and experiments
I. The New OilII. Trusted CellsIII. Global SQL QueriesIV. Cost Model and
ExperimentsV. Conclusion
PR SM 25
Unit Test Calibration
25
Internal time consumption
Eval Board•32 bit RISC CPU: 120 MHz•Crypto-coprocessor: AES, SHA•64KB RAM, 1GB NAND-Flash•USB full speed: 12 Mbps
}SMIS developped token (ZED electronics)Same technical characteristicsPrice = 50 EUR (small series)
PR SM 26
Parameters for cost model
Dataset size Ttuple : varies from 5 to 65 million
Number of groups G : varies from 1 to 106
Number of TDSs participating in the computation as a percentage of all TDSs
connected at a given time Ttds : varies from 1% to 100%).
We fix two parameters and vary the other, measuring : execution time,
parallelism of the protocol, total load, maximum load on one TDS
When the parameters are fixed :
Ttuple =106, G=103, % of TDS connected = 10% of Ttuple.
We also compute and use the optimal value for all reduction factors as
well as for.
In the figures, we plot two curves for Rnf_Noise protocols RN (nf = 2) and
WN (nf = 1000) to capture the impact of the ratio of fake tuples.
PR SM 27
EXECUTION TIME
27
Ttuple=106; G=1-106 Ttuple=5.106 - 35.106; G=1000
Naïve, noise-based, ED&EW:•G increases, Ttuple fixed Number of tuples in each group decreases•Depend only on the total number of tuples in each group (because all groups are processed in parallel) exeTime decreases when G increases.
Secure Count: •G increases time for processing the big partial aggregation increases accordingly.•Cannot fully deploy the parallel computation (cannot divide each group for TDSs in parallel, each TDS has to handle the whole G groups) exeTime increases
Naïve, RN, ED&EW:•Ttuple increases, Ttds increases accordingly not much changes
Secure Count: • Number of recursive steps increases when Ttuple increases. exeTime increase
WN,CN: • Number of fake tuples increases linearly with the number of true tuples. exeTime also increases linearly to handle the fake & true tuples
PR SM 28
NUMBER OF PARTICIPATING TDSS
28
Ttuple=106; G=1-106 Ttuple=5.106 - 35.106; G=1000
Secure Count:•G increases level of convergence is low & the size of each aggregation is big need less participating TDSs to build the aggregations to gain the high convergence level
Other solutions:• Since each group is processed in parallel and independently when G increases, the level of parallelism increases more TDSs are needed to participate in the parallel computation
WN, CN:• When true Ttuple increases, the fake tuples increases as well more TDSs are needed to process fake tuples
Secure Count:• Level of parallelism is less than other solutions needs least TDS
PR SM 29
TOTAL LOAD (NETWORK OVERHEAD)
29
Ttuple=106; G=1-106 Ttuple=5.106 - 35.106; G=1000
Noised-based:• Highest load because of the fake tuples• When G increases but Tpds does not change number of tuples (both true and fake) do not change total load is the same
Others:Lower load since handle only true data
Noised-based:• When true Ttuple increases, the fake tuples increases linearlytotal load is highest and increases
PR SM 30
MAXIMUM LOAD
30
Ttuple=106; G=1-106 Ttuple=5.106 - 35.106; G=1000
Secure Count:•When G increases, size of each aggregation is bigeach PDS process bigger aggregation•When G increases, number of participating PDSs decrease each participating PDS incurs higher loadOthers:•When G increases, number of participating PDSs decrease & number of tuples in each group decreaseseach PDS process less tuples maxLoad decrease
WN, CN: •Use all available PDSs maxLoad increases linearly when Ttuple increasesOthers:when Ttuple increases, the number of participating PDSs also increase accordingly in general, the maxLoad does not increase too much
PR SM 31
AVERAGE LOAD
31
Ttuple=106; G=1-106 Ttuple=5.106 - 35.106; G=1000
Secure Count:•Total load is unchanged but the number of participating TDSs is reduced when G increases the average load increases.WN,CN:•High total load is the same & all PTpds=10^5 participate in the computation every PDSs incur the same amount of load Others:•G increase, more participating PDSs & total load unchanged AvgLoad decreases
Although: TotalLoad(CN) > TotalLoad(SC)PTpds(CN) >> PTpds(SC)
AvgLoad(CN) < AvgLoad(SC)
PR SM 32
CONSUMED MEMORY
32
Actual RAM size of TDS
Noise-based:•Need to store only 1 group regardless of G Require least RAM.Histogram-based:•Each PDS store h groups (h>1) regardless of G Require higher RAMSC:•Each PDS store all G groups•When G increases, RAM needed increases Require highest RAM•Exceed actual RAM’s size future work
PR SM 33
AVERAGE TIME FOR PDS TO CONNECT
33
Ttuple=106; G=1-106Ttuple=5.106 - 35.106; G=1000
Secure Count:•The number of participating PDSs is reduced when G increases the average time increases.WN,CN:•High total load is unchanged & all PTpds=10^5 participate in the computation every PDSs take the same amount of time to process dataOthers:•G increase, more participating PDSs AvgTime decreases
High AvgTime:•WN,CN: because of too many fake tuples•SC: because of very few participating PDSs
PR SM 34
Theoretical Scalability
34
Tpds = 1%Ttuple Tpds = 10%Ttuple
Tpds = 100%Ttuple Secure Count: has a (low) maximum number of participants.Others: WN have higher scalability than others (in the sense that adding participants count)
PR SM 35
Experimental Scalability
PR SM 36
COMPARISON WITH OTHER STATE-OF-THE-ART METHODS
36
Hardware:•Linux workstation; •AMD Athlon-64 2Ghz processor; •512 MB memory
•SC: depends mostly on G (slightly on Ttuple)•Others: not depends on G, but mostly on Ttuple
Answering aggregation queries in a secure system model. (Ge & Zdonic, VLDB 2007)
DES: each value is decrypted and the computation is performed on the plaintext. Server must have access to secret key & plaintext (violates security requirements)
Paillier: perform computation directly on the ciphertext using a secure homomorphic encryption scheme: enc(a + b) = enc(a) + enc(b) Server performs computation without having access to the secret key or plaintext. In the end, ciphertext are passed back to the trusted agent (i.e., Key Holder) to perform a final decryption and simple calculation of the final result
PR SM 37
Metrics for the evaluation of the proposed solutions
37
Total Load
Average Time/Load
Query Response Time
Information Exposure
Throughput
Resource Variation
PR SM 38
Trade-off between criteria
38
Select ..
From ..
Where ..
Group By AG
G = card (AG)
Security: S_Agg > ED_Hist
Performance:G > 10:
ED_Hist > S_Agg
G <= 10:
ED_Hist < S_Agg
PR SMPRiSM Lab. - UMR 8144
PART V
Conclusion and perspectives
I. The New OilII. Trusted CellsIII. Global SQL QueriesIV. Cost Model and
ExperimentsV. Conclusion
PR SM 40
Short/Middle term research :Data intensive Computing on an Asymmetric Architecture
SQLQueries here do not have joins !
Take into account Malicious SSI / Broken Tokens
Field experiment on usability (with ISN)
Private/Secure MapReduceInvestigate compatibility of our protocols.
Develop new protocols.
Check performance !
XML managementAdapt the work on XQ2P (Butnaru, Gardarin, Nguyen) to the Trusted
Cells context.
Distributed Window Queries.
/41
PR SM 41
Promoting the Trusted Cells vision
Trusted Cells “Core” Open hardware and software bundle : basic functionalities
Local DB
Distributed DB
NoSQL DB
needed to develop PbD personal data management applications !
Promote an open source community around Trusted Cells.
UVSQ FabLab
Bring secure data management to the Versailles FabLab
Beyond Tamper Resistant HWResults are useable even with lower trust elements.
Include social trust / reputation.
/41
PR SMPRiSM Lab. - UMR 8144
QUESTIONS ?
42
PR SMPRiSM Lab. - UMR 8144
43
PR SM 44
top related