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.

/pdf/taintdroid-an-information-flow-tracking-system-for-realtime-3fnz9lwdso.pdf

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

Rigid body dynamics calculations are needed for many tasks in robotics, including online control. While there currently exist several competing software implementations that are sufficient for use in traditional control approaches, emerging sophisticated motion control techniques such as nonlinear model predictive control demand orders of magnitude more frequent dynamics calculations. Current software solutions are not fast enough to meet that demand for complex robots. The goal of this work is to examine the performance of current dynamics software libraries in detail. In this paper, we (i) survey current state-of-the-art software implementations of the key rigid body dynamics algorithms (RBDL, Pinocchio, RigidBodyDynamics.jl, and RobCoGen), (ii) establish a methodology for benchmarking these algorithms, and (iii) characterize their performance through real measurements taken on a modern hardware platform. With this analysis, we aim to provide direction for future improvements that will need to be made to enable emerging techniques for real-time robot motion control. To this end, we are also releasing our suite of benchmarks to enable others to help contribute to this important task.

Benchmarking and Workload Analysis of Robot Dynamics Algorithms

Fully homomorphic encryption (FHE) techniques are capable of performing encrypted computation on Boolean circuits, i.e., the user specifies encrypted inputs to the program, and the server computes on the encrypted inputs. Applying these techniques to general programs with recursive procedures and data-dependent loops has not been a focus of attention. In this paper, we take a first step toward building an interpreter that, given programs with complex control flow, schedules efficient code suitable for the application of FHE schemes.We first describe how programs written in a small Turing-complete instruction set can be executed with encrypted data and point out inefficiencies in this methodology. We then provide examples of scheduling (a) the greatest common divisor (GCD) problem using Euclid's algorithm and (b) the 3-Satisfiability (3SAT) problem using a recursive backtracking algorithm into path-levelized FHE computations. We describe how path levelization reduces control flow ambiguity and improves encrypted computation efficiency. Using these techniques and data-dependent loops as a starting point, we then build support for hierarchical programs made up of phases, where each phase corresponds to a fixed point computation that can be used to further improve the efficiency of encrypted computation.In our setting, the adversary learns an estimate of the number of steps required to complete the computation, which we show is the least amount of leakage possible.

/pdf/towards-an-interpreter-for-efficient-encrypted-computation-47g7upeliv.pdf

Towards an interpreter for efficient encrypted computation

With exascale multicores, the question of how to efficiently support a shared memory model is of paramount importance. As programmers demand the convenience of coherent shared memory, ever-growing core counts place higher demands on memory subsystems, and increasing on-chip distances mean that interconnect delays exert a significant effect on memory access latencies.

Toward a Coherent Multicore Memory Model

POWFs were introduced in [1], where they are implemented by shining a mobile laser beam through a nonhomogenous medium and observing the resulting speckle pattern. They were used to make unclonable ID cards. Indeed, an important characteristic of POWFs is that when it is difficult to reproduce the physical system or to characterize it precisely enough to simulate it, an unclonable system results.

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Secure Hardware Processors using Silicon Physical One-Way Functions

This paper proposes a technique for dynamic cache partitioning amongst simultaneously executing processes/threads. We present a general partitioning scheme that can be applied to set-associative caches at any partition granularity. Fur- thermore, in our scheme, processes/threads can have overlapping partitions, which provides more degrees of freedom when partitioning caches with low associativity. Since memory reference characteristics of processes/threads can change very quickly, our method collects the miss-rate characteristics of processes/threads at run-time, and partitions the cache amongst the executing ones. Partition sizes are varied dynamically to improve miss-rates. Trace-driven simulation results show a relative improvement in the L2 hit-rate of up to 40.5% over those generated by the standard least recently used replacement policy, and IPC improvements of up to 17%. Our results show that smart cache management and scheduling is important for CMP/SMT systems to achieve high performance.

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Srinivas Devadas

Papers

Benchmarking and Workload Analysis of Robot Dynamics Algorithms

Towards an interpreter for efficient encrypted computation

Toward a Coherent Multicore Memory Model

Secure Hardware Processors using Silicon Physical One-Way Functions

Dynamic Cache Partitioning for CMP/SMT Systems