Secure cloud data storage and access. Mihai Ordean Research Fellow University of Birmingham
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1 Secure cloud data storage and access Mihai Ordean Research Fellow University of Birmingham
2 Introduction
3 What can we encrypt?
4 What can we encrypt? DATA
5 What can we encrypt? Dynamic data DATA
6 What can we encrypt? Static data DATA
7 Protecting dynamic data Company Workstation Company Server
8 Protecting dynamic data Remote Client TLS, SSH, IPSec,. Company Server
9 Protecting dynamic data? Client TLS, SSH, IPSec,. Server
10 Protecting static data Client Cloud
11 Protecting static data
12 Protecting static data
13 Protecting static data
14 Protecting static data
15 Protecting data from the cloud Symmetric key encryption Public key encryption
16 Is encrypting data enough?
17 Analysing data access: who is the doctor?? Medical database patient records insurance records appointments
18 Analysing data access: who owns this cyphertext?? Medical database patient records insurance records appointments
19 Analysing data access Medical database patient records insurance records appointments
20 Analysing data access Medical database patient records insurance records appointments
21 Analysing data access Medical database patient records insurance records appointments
22 Analysing data access Medical database patient records insurance records appointments
23 Analysing data access Medical database patient records insurance records appointments
24 Just using encryption is not enough Content security the data is encrypted Metadata security ownership information, timestamps, access rights, cyphertext length, etc. Access pattern security when is the data accessed, who accesses the data, how is the data accessed, etc.
25 Oblivious RAM
26 Oblivious RAM (ORAM) Uses symmetric encryption (e.g. AES) to encrypt small data structures (e.g. data buckets ). Replaces specific file operations like read and write (i.e. download and upload) with a generic access operation. The access operation has a significant overhead in order to disguise the exact data being accessed.
27 PathORAM [Stefanov-van Dijk-Shi-Chan-Fletcher-Ren-Yu-Devadas13] ID1 ID2 ID3 ID27 ID10 ID16 MAP PATH_B PATH_B PATH_A PATH_D PATH_A PATH_C STASH ID1:Data1 ID27:Data27 ID10:Data10 ID16:Data16 BUCKET1 ID20:Data20 ID13:Data13 ID14:Data14 ID22:Data22 BUCKET2 BUCKET3 ID05:Data5 ID7:Data7 ID6:Data6 ID2:Data2 ID11:Data11 ID12:Data12 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID19:Data19 ID15:Data15 ID28:Data28 ID3:Data3 ID8:Data8 LEVEL 0 LEVEL 1 ID17:Data17 ID24:Data24 ID23:Data23 ID26:Data26 ID18:Data18 ID4:Data4 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2
28 PathORAM access READ: ID2:empty BUCKET1 MAP ID2 PATH_B ID20:Data20 ID13:Data13 ID14:Data14 ID22:Data22 LEVEL 0 REQUEST: PATH_B BUCKET2 ID7:Data7 BUCKET3 ID05:Data5 ID6:Data6 ID2:Data2 ID11:Data11 ID12:Data12 LEVEL 1 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID19:Data19 ID15:Data15 ID28:Data28 ID3:Data3 ID8:Data8 ID17:Data17 ID24:Data24 ID23:Data23 ID26:Data26 ID18:Data18 ID4:Data4 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2
29 PathORAM access READ: ID2:empty BUCKET1 MAP ID2 PATH_B ID20:Data20 ID13:Data13 ID14:Data14 ID22:Data22 LEVEL 0 REQUEST: PATH_B BUCKET2 ID7:Data7 BUCKET3 ID05:Data5 ID6:Data6 ID2:Data2 ID11:Data11 ID12:Data12 LEVEL 1 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID19:Data19 ID15:Data15 ID28:Data28 ID3:Data3 ID8:Data8 ID17:Data17 ID24:Data24 ID23:Data23 ID26:Data26 ID18:Data18 ID4:Data4 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2 RECEIVE: PATH_B Bucket5 Bucket2 Bucket1 ID4:Data4 ID24:Data24 ID8:Data8 ID19:Data19 ID2:Data2 ID7:Data7 ID22:Data22 ID14:Data14 ID13:Data13 ID20:Data20
30 PathORAM access READ: RECEIVE: ID2:empty PATH_B REQUEST: Bucket5 Bucket2 Bucket1 PATH_B ID4:Data4 ID24:Data24 ID8:Data8 ID19:Data19 ID2:Data2 ID7:Data7 ID22:Data22 ID14:Data14 ID13:Data13 ID20:Data20 MAP STASH BUCKET1 ID1 ID2 ID3 ID27 ID10 ID16 PATH_B PATH_C PATH_A PATH_D PATH_A PATH_C ID1:Data1 ID27:Data27 ID10:Data10 ID16:Data16 ID4:Data4 ID24:Data24 ID8:Data8 ID2:Data2 BUCKET2 BUCKET3 ID05:Data5 ID6:Data6 ID11:Data11 ID12:Data12 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID15:Data15 ID28:Data28 ID3:Data3 LEVEL 0 LEVEL 1 ID17:Data17 ID23:Data23 ID26:Data26 ID18:Data18 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2
31 PathORAM access WRITE: ID1:Data1 ID24:Data24 Bucket5 ID19:Data19 ID8:Data8 ID7:Data7 PATH_B Bucket2 ID10:Data10 ID20:Data20 ID13:Data13 ID27:Data27 ID8:Data8 Bucket1 ID4:Data4 ID16:Data16 1xPaths 3xBuckets => one per level 12xBlocks => 4 per bucket MAP BUCKET1 ID1 ID2 ID3 ID27 ID10 ID16 PATH_B PATH_C PATH_A PATH_D PATH_A PATH_C ID1:Data1 ID27:Data27 ID10:Data10 ID16:Data16 STASH ID2:Data2 ID22:Data22 BUCKET2 BUCKET3 ID05:Data5 ID6:Data6 ID11:Data11 ID12:Data12 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID15:Data15 ID28:Data28 ID3:Data3 LEVEL 0 LEVEL 1 ID17:Data17 ID23:Data23 ID26:Data26 ID18:Data18 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2
32 PathORAM structure ID1 ID2 ID3 ID27 ID10 ID16 MAP PATH_B PATH_C PATH_A PATH_D PATH_A PATH_C STASH ID2:Data2 ID22:Data22 BUCKET1 ID16:Data16 ID4:Data4 ID8:Data8 ID27:Data27 BUCKET2 BUCKET3 ID13:Data13 ID05:Data5 ID20:Data20 ID6:Data6 ID10:Data10 ID11:Data11 ID7:Data7 ID12:Data12 BUCKET4 BUCKET5 BUCKET6 BUCKET7 ID21:Data21 ID8:Data8 ID15:Data15 ID28:Data28 ID3:Data3 ID19:Data19 LEVEL 0 LEVEL 1 ID17:Data17 ID24:Data24 ID23:Data23 ID26:Data26 ID18:Data18 ID1:Data1 ID25:Data25 ID9:Data9 PATH_A PATH_B PATH_C PATH_D LEVEL 2
33 PathORAM performance Example Assuming a 128GB database with: - S = 64KB block size - Z = 5 blocks per bucket - L = 20 levels SecretDocument.txt a 1MB document stored in the database
34 PathORAM performance Example Assuming a 128GB database with: - S = 64KB block size - Z = 5 blocks per bucket - L = 20 levels What are the bandwidth requirements to access this document? SecretDocument.txt a 1MB document stored in the database
35 PathORAM performance Example Assuming a 128GB database with: - S = 64KB block size - Z = 5 blocks per bucket - L = 20 levels 1MB = 1024KB Block per document N: N = 1024KB/64KB (size of the block) = 16 SecretDocument.txt a 1MB document stored in the database
36 PathORAM performance Example Assuming a 128GB database with: - S = 64KB block size - Z = 5 blocks per bucket - L = 20 levels SecretDocument.txt a 1MB document stored in the database 1MB = 1024KB Block per document N: N = 1024KB/64KB (size of the block) = 16 To send/receive ONE document ORAM requires: N*S*Z*L = 100MB
37 ORAM applications Personal health records Credit score systems GENOME related research Private information retrieval (PIR) protocols
38 Searchable encryption
39 Searchable Encryption
40 Searching For each document in the database: For each word in document: if word = top-secret exit for print document-id
41 Encrypting databases word document-id document ENCRYPTED word ENCRYPTED document-id WORD DOCUMENT-ID Database Encrypted database
42 Searchable Encryption KEYWORDS: CIA WATERGATE NIXON US GCHQ GB Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB Efficiency of the index Number of documents increases => time increases Number of keywords increases => time increases
43 Searchable Encryption KEYWORDS: CIA WATERGATE NIXON US GCHQ GB Inverted index CIA WATERGATE NIXON US GCHQ GB Efficiency of the index Number of keywords increases => time increases
44 What do we want to protect? What we search for KEYWORDS: CIA WATERGATE What is the result of the search query DOCUMENT NAMES: How often we search for something 1: 2: n: CIA
45 Padding Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB
46 Padding Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB
47 Padding Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB Inverted index CIA WATERGATE NIXON US GCHQ GB
48 Padding Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB Inverted index CIA WATERGATE NIXON US GCHQ GB
49 Intersections, again Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB
50 Intersections, again Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB
51 Intersections, again Forward index CIA WATERGATE US GCHQ GB CIA NIXON US GCHQ GB CIA CIA CIA CIA
52 Server the computation 1. Client work needs to be as low as possible. 2. Server needs to do most of the search work.
53 Secure searching Inverted index: CIA WATERGATE NIXON Symmetric key searchable encryption index: ENC. DOC. NAMES: INDEX: CIA CIA CIA
54 Secure searching Server has: ENC. DOC. NAMES: INDEX: CIA CIA CIA Search term:
55 Secure searching Server has: ENC. DOC. NAMES: INDEX: CIA CIA CIA Search term: Server computation:
56 Secure searching Server has: ENC. DOC. NAMES: INDEX: CIA CIA CIA Search term: Server computation:
57 Secure searching Server has: ENC. DOC. NAMES: INDEX: CIA CIA CIA Search term: Server computation:
58 Secure searching Server has: ENC. DOC. NAMES: INDEX: CIA CIA CIA Search term: Server computation: Result:
59 Performance Example 1 - OXT: [Cash-Jarecki-Jutla-Krawczyk-Rosu-Steiner13] Encrypted database size: 13GB DB Contents: 1.5 million s & attachments Avg. search time: less than 500ms Example 2 2Lev: [Cash-Jaeger-Jarecki-Jutla-Krawczyk-Steiner-Rosu14] Encrypted database size: 900GBs Setup time: 16 hours Avg. query time: less than 200ms
60 Searchable encryption limitations Encrypted search term is deterministic Access pattern is not completely hidden Setting up the index requires a significant amount of time Most schemes do not support index extensions
61 ORAM vs. Searchable Encryption ORAM Searchable encryption Provides anonymous access to data blocks Used in private information retrieval protocols Fully protects access patterns and data contents Requires a considerable overheads which greatly limit usability Enables users to securely search a precomputed index Used to efficiently locate data in large databases Protects search terms and search results Does not fully protect access patterns
62 Thank you!
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