1,270 research outputs found
Sphinx: A Secure Architecture Based on Binary Code Diversification and Execution Obfuscation
Sphinx, a hardware-software co-design architecture for binary code and
runtime obfuscation. The Sphinx architecture uses binary code diversification
and self-reconfigurable processing elements to maintain application
functionality while obfuscating the binary code and architecture states to
attackers. This approach dramatically reduces an attacker's ability to exploit
information gained from one deployment to attack another deployment. Our
results show that the Sphinx is able to decouple the program's execution time,
power and memory and I/O activities from its functionality. It is also
practical in the sense that the system (both software and hardware) overheads
are minimal.Comment: Boston Area Architecture 2018 Workshop (BARC18
Sphinx: a secure architecture based on binary code diversification and execution obfuscation
Sphinx, a hardware-software co-design architecture for binary code and runtime obfuscation. The Sphinx architecture uses binary code diversification and self-reconfigurable processing elements to maintain application functionality while obfuscating the binary code and architecture states to attackers. This approach dramatically reduces an attackerās ability to exploit information gained from one deployment to attack another deployment. Our results show that the Sphinx is able to decouple the programās execution time, power and memory and I/O activities from its functionality. It is also practical in the sense that the system (both software and hardware) overheads are minimal.Published versio
Runtime protection via dataļ¬ow flattening
Software running on an open architecture, such as the PC, is vulnerable to inspection and modiļ¬cation. Since software may process valuable or sensitive information, many defenses against data analysis and modiļ¬cation have been proposed. This paper complements existing work and focuses on hiding data location throughout program execution. To achieve this, we combine three techniques: (i) periodic reordering of the heap, (ii) migrating local variables from the stack to the heap and (iii) pointer scrambling. By essentialy flattening the dataflow graph of the program, the techniques serve to complicate static dataflow analysis and dynamic data tracking. Our methodology can be viewed as a data-oriented analogue of control-flow flattening techniques. Dataflow flattening is useful in practical scenarios like DRM, information-flow protection, and exploit resistance. Our prototype implementation compiles C programs into a binary for which every access to the heap is redirected through a memory management unit. Stack-based variables may be migrated to the heap, while pointer accesses and arithmetic may be scrambled and redirected. We evaluate our approach experimentally on the SPEC CPU2006 benchmark suit
Affine Determinant Programs: A Framework for Obfuscation and Witness Encryption
An affine determinant program ADP: {0,1}^n → {0,1} is specified by a tuple (A,B_1,...,B_n) of square matrices over F_q and a function Eval: F_q → {0,1}, and evaluated on x \in {0,1}^n by computing Eval(det(A + sum_{i \in [n]} x_i B_i)).
In this work, we suggest ADPs as a new framework for building general-purpose obfuscation and witness encryption. We provide evidence to suggest that constructions following our ADP-based framework may one day yield secure, practically feasible obfuscation.
As a proof-of-concept, we give a candidate ADP-based construction of indistinguishability obfuscation (iO) for all circuits along with a simple witness encryption candidate. We provide cryptanalysis demonstrating that our schemes resist several potential attacks, and leave further cryptanalysis to future work. Lastly, we explore practically feasible applications of our witness encryption candidate, such as public-key encryption with near-optimal key generation
Genetic Improvement for Code Obfuscation
Genetic improvement (GI) is a relatively new area of software engineering and thus the extent of its applicability is yet to be explored. Although a growing interest in GI in recent years started with the work on automatic bug fixing, the area flourished when results on optimisation of non-functional software properties, such as efficiency and energy consumption, were published. Further success of GI in transplanting functionality from one program to another leads to a question: what other software engineering areas can benefit from the use of genetic improvement techniques? We propose to utilise GI for code obfuscation
Secret-Sharing for NP
A computational secret-sharing scheme is a method that enables a dealer, that
has a secret, to distribute this secret among a set of parties such that a
"qualified" subset of parties can efficiently reconstruct the secret while any
"unqualified" subset of parties cannot efficiently learn anything about the
secret. The collection of "qualified" subsets is defined by a Boolean function.
It has been a major open problem to understand which (monotone) functions can
be realized by a computational secret-sharing schemes. Yao suggested a method
for secret-sharing for any function that has a polynomial-size monotone circuit
(a class which is strictly smaller than the class of monotone functions in P).
Around 1990 Rudich raised the possibility of obtaining secret-sharing for all
monotone functions in NP: In order to reconstruct the secret a set of parties
must be "qualified" and provide a witness attesting to this fact.
Recently, Garg et al. (STOC 2013) put forward the concept of witness
encryption, where the goal is to encrypt a message relative to a statement "x
in L" for a language L in NP such that anyone holding a witness to the
statement can decrypt the message, however, if x is not in L, then it is
computationally hard to decrypt. Garg et al. showed how to construct several
cryptographic primitives from witness encryption and gave a candidate
construction.
One can show that computational secret-sharing implies witness encryption for
the same language. Our main result is the converse: we give a construction of a
computational secret-sharing scheme for any monotone function in NP assuming
witness encryption for NP and one-way functions. As a consequence we get a
completeness theorem for secret-sharing: computational secret-sharing scheme
for any single monotone NP-complete function implies a computational
secret-sharing scheme for every monotone function in NP
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