Ramya Prabhakar, Seung Woo Son, Christina Patrick, Sri Hari Krishna Narayanan, Mahmut Kandemir Pennsylvania State University 4th International IEEE Security in Storage Workshop ‘07 27 th September, 2007 Securing Disk-Resident Data through Application Level Encryption Ramya Prabhakar

Outline Motivation Motivation for Application Level Encryption Re-Use Evaluate a reuse distance oriented approach for selective encryption of disk-resident data Profiling A profile-guided approach that approximates the behavior of the reuse distance oriented approach Analysis Quantitative analysis of the trade-offs between confidentiality and performance Conclusion Summarize the major observations and results

Motivation File System based approaches The performance impact can be a showstopper File-level encryption solutions have course granularity Data-Access characteristics of Applications Frequent reuse Volatile and disk resident data

Data Reuse in Applications Eg. Matrix – Matrix Multiplication A X B = C Matrix B is read every time an element of C is computed = X

Reuse Potential Reuse potential is a measure of amount of data read/written repeatedly by the application Different applications have different reuse potentials

The Two Extremes… Always Encrypt/DecryptNever Encrypt/Decrypt Minimum Vulnerability Factor Maximum security Maximum I/O Time Significant Performance overhead Minimum I/O Time Significant Performance improvement Maximum exposure Maximum Vulnerability Factor

Reuse oriented approach write_encrypt (…, offset) write_encrypt (…, offset) read_decrypt (…, offset) read_decrypt (…, offset) read_decrypt (…, offset) write_encrypt (…, offset) read_decrypt (…, offset) read_decrypt (…, offset) Reuse distance( δ ) δ threshold plain_write(…, offset) plain_read(…, offset)

Distribution of Reuse

Metrics of Interest I/O Time (IOT) I/O latency when encryption/ decryption is included. Normalized to base version Vulnerability Factor (VF) percentage of data stored in plain text during execution Two variants: Average Vulnerability Factor (AVF) Maximum Vulnerability Factor (MVF) Ideal case reduce both IOT and VF

Metrics Vs Reuse Distance NED DES scheme reduces IOT over AED DES by 74% NED DES scheme reduces IOT over AED DES by 26%

But… Reuse oriented approach is idealistic Analysis is perfect; derives maximum benefit Requires knowledge of future references Not possible to implement

Profile Guided Approach Profiling Collect statistical information Obtain dynamic behavior of each static call An implementable method to approximate reuse- oriented approach Static I/O call results in many dynamic instances of the same call

Profile Guided Approach

Profiler inserts hints to every static call Three types of static calls: Group I Always interpreted as read_decrypt / write_encrypt Group II Always interpreted as plain_read / plain_write Group III Decision varies dynamically. Non-deterministic

Profile Guided Approach Distribution of static I/O calls among groups

I/O Call Splitting

Group III references optimized in two ways Performance oriented approach (PO) Profiles with higher δ threshold Performance is favored in the tradeoff Security oriented approach (SO) Profiles with higher δ threshold Performance is favored in the tradeoff

Results Variation of IOT(DES) with different approaches

Results Variation of IOT(AES) with different approaches

Results Variation of AVF with different approaches

Results Variation of MVF with different approaches

Guidelines for suitable δthreshold Performance ratio for δk is IOT for lowest δ divided by IOT for δk Security ratio for δk is portion of secure data at δk divided by portion of secure data for highest δ Combined metric is Performance ratio divided by security ratio At δk represents unit gain in performance for unit loss in security CM is less than, equal to or greater than 1

Conclusion Quantitative analysis of performance and confidentiality tradeoff Disk resident data remains secured Encryption/decryption overheads significantly reduced 46.5% with 3-DES 30.63% with AES

IO Time contribution to overall execution latency is between 64.2% and 96.6%. The absolute IOT values measured for base version are , , , and msec for swim, mgrid, lu, mxm and tsf respectively.

Characteristics of Applications