Biblio

Thumbnail preserving encryption (TPE) was suggested by Wright et al. [Information Hiding & Multimedia Security Workshop 2015] as a way to balance privacy and usability for online image sharing. The idea is to encrypt a plaintext image into a ciphertext image that has roughly the same thumbnail as well as retaining the original image format. At the same time, TPE allows users to take advantage of much of the functionality of online photo management tools, while still providing some level of privacy against the service provider. In this work we present two new approximate TPE encryption schemes. In our schemes, ciphertexts and plaintexts have perceptually similar, but not identical, thumbnails. Our constructions are the first TPE schemes designed to work well with JPEG compression. In addition, we show that they also have provable security guarantees that characterize precisely what information about the plaintext is leaked by the ciphertext image. We empirically evaluate our schemes according to the similarity of plaintext & ciphertext thumbnails, increase in file size under JPEG compression, preservation of perceptual image hashes, among other aspects. We also show how approximate TPE can be an effective tool to thwart inference attacks by machine-learning image classifiers, which have shown to be effective against other image obfuscation techniques.

Bulk electric systems include hundreds of synchronous generators. Faults in such systems can induce oscillations in the generators which if not detected and controlled can destabilize the system. Mode estimation is a popular method for oscillation detection. In this paper, we propose a resilient algorithm to estimate electro-mechanical oscillation modes in large scale power system in the presence of false data. In particular, we add a fault tolerance mechanism to a variant of alternating direction method of multipliers (ADMM) called S-ADMM. We evaluate our method on an IEEE 68-bus test system under different attack scenarios and show that in all the scenarios our algorithm converges well.

State estimation plays a critically important role in ensuring the secure and reliable operation of the electric grid. Recent works have shown that the state estimation process is vulnerable to stealthy attacks where an adversary can alter certain measurements to corrupt the solution of the process, but evade the existing bad data detection algorithms and remain invisible to the system operator. Since the state estimation result is used to compute optimal power flow and perform contingency analysis, incorrect estimation can undermine economic and secure system operation. However, an adversary needs sufficient resources as well as necessary knowledge to achieve a desired attack outcome. The knowledge that is required to launch an attack mainly includes the measurements considered in state estimation, the connectivity among the buses, and the power line admittances. Uncertainty in information limits the potential attack space for an attacker. This advantage of uncertainty enables us to apply moving target defense (MTD) strategies for developing a proactive defense mechanism for state estimation.

In this paper, we propose an MTD mechanism for securing state estimation, which has several characteristics: (i) increase the knowledge uncertainty for attackers, (ii) reduce the window of attack opportunity, and (iii) increase the attack cost. In this mechanism, we apply controlled randomization on the power grid system properties, mainly on the set of measurements that are considered in state estimation, and the topology, especially the line admittances. We thoroughly analyze the performance of the proposed mechanism on the standard IEEE 14- and 30-bus test systems.