Privacy-Preserving Forensics

Transcription

1 DIGITAL FORENSIC RESEARCH CONFERENCE Privacy-Preserving Forensics By Frederik Armknecht, Andreas Dewald and Michael Gruhn Presented At The Digital Forensic Research Conference DFRWS 2015 USA Philadelphia, PA (Aug 9 th - 13 th ) DFRWS is dedicated to the sharing of knowledge and ideas about digital forensics research. Ever since it organized the first open workshop devoted to digital forensics in 2001, DFRWS continues to bring academics and practitioners together in an informal environment. As a non-profit, volunteer organization, DFRWS sponsors technical working groups, annual conferences and challenges to help drive the direction of research and development.

2 Privacy-Preserving Forensics (PPEF) Frederik Armknecht (University of Mannheim), Andreas Dewald (Friedrich-Alexander University) Sven Kälber (Speaker, Friedrich-Alexander University)

3 Agenda Idea, motivation & contributions The big picture / overall scheme design Own implementation details Protection mechanism Extraction mechanism Cryptographic building blocks Practical implementation and evaluation Summary & conclusion Limitations & future work 8/22/15 2

4 Idea of PPEF Privacy protection of employees in (large-scale) digital forensic investigations Revealing of only case relevant information Achieved through strong cryptographic standards Operation principle: 1. Extraction of mailboxes 2. Encryption of all s by applying our introduced scheme 3. Hand over of only encrypted mailboxes to third-party investigators 4. Decryption of individual s only possible on t matching keywords 8/22/15 3

5 Motivation Private use of corporate accounts Private s typically contain private and very sensitive data This information is often highly protected by local data protection laws Typically case irrelevant information in private s Todays approaches and tools are often limited to filtering, which is not enforced Investigators might read private s by accident or on purpose Case United States v. Carey (1999) Problems of leaving the s at the company s IT Leaks search queries of investigators Is costly and time consuming because of the high degree of interaction needed Trust issues 8/22/15 4

6 Contributions Novel approach for privacy-preserving forensics allowing for non-interactive theshold keyword search on encrypted s Proof-of-concept implementation in Python and as a Autopsy v3 plug-in An evaluation of the practical applicability in terms of: en- / decryption runtime performance introduced storage overhead brute-force / dicitonary attack vulnerability 8/22/15 5

7 Encryption Encrypt Encryption of contains: The encrypted text Secret A compact representation of the Sharing mapping Extract Keywords Keyword 1 Keyword 2 Keyword n Mapping Key Share 1 Key Share 2 Key Share n 8/22/15 6

8 Decryption success Decrypt Secret Sharing Searchs for Keyword 1 Keyword 2 Keyword t Mapping Key Share 1 Key Share 2 Key Share n 8/22/15 7

9 Decryption fail Decrypt Secret Sharing Searchs for Correct kw 1 Correct kw 2 Wrong kw t Mapping Key Share 1 Key Share 2 Key Share n 8/22/15 8

10 Details Encryption of s (protection mechanism): Each plaintext P is encrypted to a cyphertext C with an individual secret key k. k gets split up in shares and might later be reconstructed Support for blacklisting of commonly used words (e.g. the ) Support for whitelisting of investigation keywords (e.g. fraud ) Decryption of s (extraction mechanism): Only possible when the in question contains at least t keywords. Investigator learns nothing about the secret key of other s upon successfully decrypting one . Investigator learns nothing about the content of the mail if t-1 or less keywords match the content of the . 8/22/15 9

11 Cryptographic Building Blocks 1. Encryption function: AES-128 in CBC mode used for the encryption of individual s Add characteristic padding p as the first block to be decrypted (e.g. [0,...,0]) 2. Shamir s Secrect Sharing Used for splitting the secret key k into shares Details follow on the next slide 3. Mapping Hash function: SHA-256 part of the mapping function Further tweaks for efficiency reasons 8/22/15 10

12 Shamir s Secret Sharing Functionality: Input: Two integers t n, a secret k Output: n shares " 1,, " % Security Given at least t shares, one can reconstruct the secret If less than t shares are known, reconstruction not possible Realization Polynomial interpolation of a polynomial of degree t-1 8/22/15 11

13 Working Principle Input: Two integers t n, Secret k Choose polynomial &(') of degree t-1 Compute shares: ( ' 1, ) 1 ),,( ' %, ) % ) (here: %=12) with ) + =&( ' + ) Reconstruction from t shares: Interpolate &(') Compute "=&( ' 0 ), y 1 y 3 y 8 y 12 y 2 y 4 y 7 y 9 y y5 y 11 6 y10 p(x) x 0 x 1 x 12 8/22/15 12

14 The Mapping Function Text Keyword 1 Keyword 2 Keyword n? Mapping 256-bit Strings Key Share 1 Key Share 2 Key Share n SHA 256 Hashfunction 256-bit Strings Hash value 1 Hash value 2 Hash value t Function G 8/22/15 13

15 Function G Task: Map 256-bit hash values to 256-bit shares (x!i, y!i) Approach: Interpret hash values as (x!i, z!i) (128-bit bit) Use the values x!i to compute shares (x!i, y!i) = (x!i, p(x!i)) Find mapping g(x) such that g(x!i) = y!i XOR z!i Function G(h!i) = G(x!i,z!i) = (x!i, g(x!i) XOR z!i)! (x!i, y!i) Getting the mapping: Core idea: compute polynomial g(x) such that g(x!i)=y!i XOR z!i Problem: requires to interpolate polynomial of degree n! effort is O(n"3), too slow Idea: Split range of x into l subsets, e.g. determined by the l last bits Interpolate polynomials g!j(x) for each subset Effort: interpolate l polynomials, each of degree n/l Overall effort: l " (n / l)"3 = n"3 / l"2 8/22/15 14

16 Practical implementation 1. Python en- / decrytion of mailboxes Supported mailbox formats: mbox pst MH (RFC 822) Maildir 2. PPEF plugin for Autopsy v3 8/22/15 15

17 PPEF Autopsy plug-in 8/22/15 16

18 PPEF Autopsy plug-in 8/22/15 17

19 PPEF Autopsy plug-in 8/22/15 18

20 PPEF Autopsy plug-in 8/22/15 19

21 PPEF Autopsy plug-in 8/22/15 20

22 Evaluation The data set used in our evaluation consists of 5 different mailboxes: Apache httpd user mailing list ( s) Work personal work s (1590 s) A, B, C private accounts (511, 349, 83 s) Evaluations: 1. Encryption runtime performance 2. Encryption storage overhead 3. Search / decryption runtime performance 4. Brute-force attack performance 8/22/15 21

23 Encryption Performance Time (in seconds) to encrypt the corresponding s of each account Average encryption rate: s/sec Encryption of large mailboxes might take several hours (< 2h for s), but only needs to be done once! 8/22/15 22

24 Encryption storage overhead Encryption with AES does not add much storage overhead (33 48 bytes per mail) Main storage overhead factor is the mapping function (on average bytes per mail) Average storage overhead: 5.2 % 8/22/15 23

25 Search / decryption performance Time (in seconds) to search each for 3 keywords and decrypt matching s Average search and decryption rate: 98 mails/sec Searches on large mailboxes take time (< 15min) but are still feasible 8/22/15 24

26 Attack performance evaluation Brute-force attacks to decrypt the whole mailbox (π=0.99) or a random half of the mailbox (π=0.5) Using 4 different vocabularies Oxford English Dictionary (171,476 words) 50 % of the Oxford English Dictionary (85,738 words) Vocabulary in daily speech edu. person (20,000 words) Vocabulary of uneducated person (10,000 words) 8/22/15 25

27 Summary / Conclusion We proposed a novel approach for privacypreserving forensics allowing for noninteractive theshold keyword search on encrypted s. We developed a prototype-implementation in Python and an Autopsy plug-in that supports multiple well-known mailbox formats. We evaluated the practical applicability in terms of en- / decryption performance, storage overhead and brute-force vulnerability. Sufficiently large mailboxes are well protected against dictionary (brute-force) attacks 8/22/15 26

28 Limitations / Future work Limitations: Scheme based on keyword searches, therefore prone to spelling errors No wildcard operator or regular expression possible that allows for more advanced search queries Brute-force / dictionary attacks possible Future work: Support for wildcard usage within the search keywords 8/22/15 27

29 Thank you for your attention! Speaker: Sven Kälber - sven.kaelber@cs.fau.de Authors: Frederik Armknecht armknecht@uni-mannheim.de Andreas Dewald andreas.dewald@fau.de

30 8/22/15 29

31 Function G Task: Map 256-bit hash values to 256-bit key shares Approach Interpret hash values as (128 bit bit) Use the values to compute key shares Find a mapping such that Function Getting the Mapping Core idea: compute polynomial such that Problem: requires to interpolate polynomial of degree n effort is in, too Idea: Split range of into subsets, e.g., determined by the first bits Interpolate polynomials for each subset Effort: interpolate polynomials, each of degree. Overall effort: 8/22/15 30