Today’s smartphone operating systems frequently fail to provide users with visibility into how third-party applications collect and share their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid enables realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 32p performance overhead on a CPU-bound microbenchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, in our 2010 study we found 20 applications potentially misused users’ private information; so did a similar fraction of the tested applications in our 2012 study. Monitoring the flow of privacy-sensitive data with TaintDroid provides valuable input for smartphone users and security service firms seeking to identify misbehaving applications.

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TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

Today's smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android's virtualized execution environment. TaintDroid incurs only 14% performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users' private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications.

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TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones

Ordered Binary-Decision Diagrams (OBDDs) represent Boolean functions as directed acyclic graphs. They form a canonical representation, making testing of functional properties such as satisfiability and equivalence straightforward. A number of operations on Boolean functions can be implemented as graph algorithms on OBDD data structures. Using OBDDs, a wide variety of problems can be solved through symbolic analysis. First, the possible variations in system parameters and operating conditions are encoded with Boolean variables. Then the system is evaluated for all variations by a sequence of OBDD operations. Researchers have thus solved a number of problems in digital-system design, finite-state system analysis, artificial intelligence, and mathematical logic. This paper describes the OBDD data structure and surveys a number of applications that have been solved by OBDD-based symbolic analysis.

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Symbolic Boolean manipulation with ordered binary-decision diagrams

Physical Unclonable Functions (PUFs) are innovative circuit primitives that extract secrets from physical characteristics of integrated circuits (ICs). We present PUF designs that exploit inherent delay characteristics of wires and transistors that differ from chip to chip, and describe how PUFs can enable low-cost authentication of individual ICs and generate volatile secret keys for cryptographic operations.

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Physical unclonable functions for device authentication and secret key generation

Physical Unclonable Functions for Device Authentication and Secret Key Generation

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The Art of Labeling: Task Augmentation for Private(Collaborative) Learning on Transformed Data.

We build and evaluate Tiny ORAM, an Oblivious RAM prototype on FPGA. Oblivious RAM is a cryptographic primitive that completely obfuscates an application’s data, access pattern, and read/write behavior to/from external memory (such as DRAM or disk). Tiny ORAM makes two main contributions. First, by removing an algorithmic bottleneck in prior work, Tiny ORAM is the first hardware ORAM design to support arbitrary block sizes (e.g., 64 Bytes to 4096 Bytes). With a 64 Byte block size, Tiny ORAM can finish an access in 1.4μs, over 40× faster than the prior-art implementation. Second, through novel algorithmic and engineering-level optimizations, Tiny ORAM reduces the number of symmetric encryption operations by ∼ 3× compared to a prior work. Tiny ORAM is also the first design to implement and report real numbers for the cost of symmetric encryption in hardware ORAM constructions. Putting it together, Tiny ORAM requires 18381 (5%) LUTs and 146 (13%) Block RAM on a Xilinx XC7VX485T FPGA, including the cost of encryption.

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A Low-Latency, Low-Area Hardware Oblivious RAM Controller

We build and evaluate Tiny ORAM, an Oblivious RAM prototype on FPGA. Oblivious RAM is a cryptographic primitive that completely obfuscates an application's data, access pattern, and read/write behavior to/from external memory (such as DRAM or disk). Tiny ORAM makes two main contributions. First, by removing an algorithmic bottleneck in prior work, Tiny ORAM is the" first hardware ORAM design to support arbitrary block sizes (e.g., 64 Bytes to 4096 Bytes). With a 64 Byte block size, Tiny ORAM can " finish an access in 1:4us, over 40x faster than the prior-art implementation. Second, through novel algorithmic and engineering-level optimizations, Tiny ORAM reduces the number of symmetric encryption operations by a#x007E; 3x compared to a prior work. Tiny ORAM is also the " first design to implement and report real numbers for the cost of symmetric encryption in hardware ORAM constructions. Putting it together, Tiny ORAM requires 18381 (5%) LUTs and 146 (13%) Block RAM on a Xilinx XC7VX485T FPGA, including the cost of encryption.

Existing single-stream logging schemes are unsuitable for in-memory database management systems (DBMSs) as the single log is often a performance bottleneck. To overcome this problem, we present Taurus, an efficient parallel logging scheme that uses multiple log streams, and is compatible with both data and command logging. Taurus tracks and encodes transaction dependencies using a vector of log sequence numbers (LSNs). These vectors ensure that the dependencies are fully captured in logging and correctly enforced in recovery. Our experimental evaluation with an in-memory DBMS shows that Taurus's parallel logging achieves up to 9.9x and 2.9x speedups over single-streamed data logging and command logging, respectively. It also enables the DBMS to recover up to 22.9x and 75.6x faster than these baselines for data and command logging, respectively. We also compare Taurus with two state-of-the-art parallel logging schemes and show that the DBMS achieves up to 2.8x better performance on NVMe drives and 9.2x on HDDs.

Taurus: Lightweight Parallel Logging for In-Memory Database Management Systems (Extended Version).

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Srinivas Devadas

Papers

The Art of Labeling: Task Augmentation for Private(Collaborative) Learning on Transformed Data.

A Low-Latency, Low-Area Hardware Oblivious RAM Controller

A Low-Latency, Low-Area Hardware Oblivious RAM Controller

Taurus: Lightweight Parallel Logging for In-Memory Database Management Systems (Extended Version).

Spectrum: High-Bandwidth Anonymous Broadcast with Malicious Security.