Exchanging registered users’ submitting reviews towards trajectory privacy preservation for review services in Location-Based Social Networks

In Location-Based Social Networks (LBSNs), registered users submit their reviews for visited point-of-interests (POIs) to the system providers (SPs). The SPs anonymously publish submitted reviews to build reputations for POIs. Unfortunately, the user profile and trajectory contained in reviews can be easily obtained by adversaries who SPs has compromised with. Even worse, existing techniques, such as cryptography and generalization, etc., are infeasible due to the necessity of public publication of reviews and the facticity of reviews. Inspired by pseudonym techniques, we propose an approach to exchanging reviews before users submit reviews to SPs. In our approach, we introduce two attacks, namely review-based location correlation attack (RLCA) and semantic-based long-term statistical attack (SLSA). RLCA can be exploited to link the real user by reconstructing the trajectory, and SLSA can be launched to establish a connection between locations and users through the difference of semantic frequency. To resist RLCA, we design a method named User Selection to Resist RLCA (USR-RLCA) to exchange reviews. We propose a metric to measure the correlation between a user and a trajectory. Based on the metric, USR-RLCA can select reviews resisting RLCA to exchange by suppressing the number of locations on each reconstructed trajectory below the correlation. However, USR-RLCA fails to resist SLSA because of ignoring the essential semantics. Hence, we design an enhanced USR-RLCA named User Selection to Resist SLSA (USR-SLSA). We first propose a metric to measure the indistinguishability of locations concerning the difference of semantic frequency in a long term. Then, USR-SLSA can select reviews resisting SLSA to exchange by allowing two reviews whose indistinguishability is below the probability difference after the exchange to be exchanged. Evaluation results verify the effectiveness of our approach in terms of privacy and utility.

f-Slip: An Efficient Privacy-Preserving Data Publishing Framework for 1: M Microdata with Multiple Sensitive Attributes.

Privacy of the individuals plays a vital role when a dataset is disclosed in public. Privacy-preserving data publishing is a process of releasing the anonymized dataset for various purposes of analysis and research. The data to be published contain several sensitive attributes such as diseases, salary, symptoms, etc. Earlier, researchers have dealt with datasets considering it would contain only one record for an individual [1:1 dataset], which is uncompromising in various applications. Later, many researchers concentrate on the dataset, where an individual has multiple records [1:M dataset]. In the paper, a model f-slip was proposed that can address the various attacks such as Background Knowledge (bk) attack, Multiple Sensitive attribute correlation attack (MSAcorr), Quasi-identifier correlation attack(QIcorr), Non-membership correlation attack(NMcorr) and Membership correlation attack(Mcorr) in 1:M dataset and the solutions for the attacks. In f -slip, the anatomization was performed to divide the table into two subtables consisting of i) quasi-identifier and ii) sensitive attributes. The correlation of sensitive attributes is computed to anonymize the sensitive attributes without breaking the linking relationship. Further, the quasi-identifier table was divided and k-anonymity was implemented on it. An efficient anonymization technique, frequency-slicing (f-slicing), was also developed to anonymize the sensitive attributes. The f -slip model is consistent as the number of records increases. Extensive experiments were performed on a real-world dataset Informs and proved that the f -slip model outstrips the state-of-the-art techniques in terms of utility loss, efficiency and also acquires an optimal balance between privacy and utility.

Comparing Large-unit and Bitwise Linear Approximations of SNOW 2.0 and SNOW 3G and Related Attacks

In this paper, we study and compare the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G, and present a fast correlation attack on SNOW 3G by using our newly found bitwise linear approximations. On one side, we reconsider the relation between the large-unit linear approximation and the smallerunit/ bitwise ones derived from the large-unit one, showing that approximations on large-unit alphabets have advantages over all the smaller-unit/bitwise ones in linear attacks. But then on the other side, by comparing the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G respectively, we have found many concrete examples of 8-bit linear approximations whose certain 1-dimensional/bitwise linear approximations have almost the same SEI (Squared Euclidean Imbalance) as that of the original 8-bit ones. That is, each of these byte-wise linear approximations is dominated by a single bitwise approximation, and thus the whole SEI is not essentially larger than the SEI of the dominating single bitwise approximation. Since correlation attacks can be more efficiently implemented using bitwise approximations rather than large-unit approximations, improvements over the large-unit linear approximation attacks are possible for SNOW 2.0 and SNOW 3G. For SNOW 3G, we make a careful search of the bitwise masks for the linear approximations of the FSM and obtain many mask tuples which yield high correlations. By using these bitwise linear approximations, we mount a fast correlation attack to recover the initial state of the LFSR with the time/memory/data/pre-computation complexities all upper bounded by 2174.16, improving slightly the previous best one which used an 8-bit (vectorized) linear approximation in a correlation attack with all the complexities upper bounded by 2176.56. Though not a significant improvement, our research results illustrate that we have an opportunity to achieve improvement over the large-unit attacks by using bitwise linear approximations in a linear approximation attack, and provide a newinsight on the relation between large-unit and bitwise linear approximations.

Who Can Find My Devices? Security and Privacy of Apple’s Crowd-Sourced Bluetooth Location Tracking System

Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world’s largest crowd-sourced location tracking network called o~ine finding (OF). OF leverages online finder devices to detect the presence of missing o~ine devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, prevent tracking of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user’s top locations with an error in the order of 10 meters in urban areas. While we find that OF’s design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available.

Resistance of SNOW-V against Fast Correlation Attacks

SNOW-V is a new member in the SNOW family of stream ciphers, hoping to be competitive in the 5G mobile communication system. In this paper, we study the resistance of SNOW-V against bitwise fast correlation attacks by constructing bitwise linear approximations. First, we propose and summarize some efficient algorithms using the slice-like techniques to compute the bitwise linear approximations of certain types of composition functions composed of basic operations like ⊞, ⊕, Permutation, and S-box, which have been widely used in word-oriented stream ciphers such as SNOW-like ciphers. Then, using these algorithms, we find a number of stronger linear approximations for the FSM of the two variants of SNOW-V given in the design document, i.e., SNOW-V σ0 and SNOW-V⊞8, ⊞8. For SNOW-V σ0, where there is no byte-wise permutation, we find some bitwise linear approximations of the FSM with the SEI (Squared Euclidean Imbalance) around 2−37.34 and mount a bitwise fast correlation attack with the time complexity 2251.93 and memory complexity 2244, given 2103.83 keystream outputs, which improves greatly the results in the design document. For SNOW-V⊞8, ⊞8, where both of the two 32-bit adders in the FSM are replaced by 8-bit adders, we find our best bitwise linear approximations of the FSM with the SEI 2−174.14, while the best byte-wise linear approximation in the design document of SNOW-V has the SEI 2−214.80. Finally, we study the security of a closer variant of SNOW-V, denoted by SNOW-V⊞32, ⊞8, where only the 32-bit adder used for updating the first register is replaced by the 8-bit adder, while everything else remains identical. For SNOW-V⊞32, ⊞8, we derive many mask tuples yielding the bitwise linear approximations of the FSM with the SEI larger than 2−184. Using these linear approximations, we mount a fast correlation attack with the time complexity 2377.01 and a memory complexity 2363, given 2253.73 keystream outputs. Note that neither of our attack threatens the security of SNOW-V. We hope our research could further help in understanding bitwise linear approximation attacks and also the structure of SNOW-like stream ciphers.

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

Crypt-OR:A privacy-preserving system for exemplar-based object-removal over the cloud

<div>Object removal is a technique for removing the undesired object(s) and then fill-in the empty region(s) in an image such that the modified image is visually plausible. The existing algorithms are unable to provide promising results when the region to be removed - has varying textured-neighborhood, is small in size and the depth of the image and, is of specific geometric shapes such as triangle</div><div>and rectangle. In this paper, we proposed a new algorithm by incorporating the merits of partial differential equations (PDEs) and exemplar-based schemes to address these challenges. The data term, which measures the continuity of</div><div>isophotes in exemplar-based methods, is modified by incorporating a regularizer term and partial derivatives up to second order of the input image. This regularizer enhances the strength of isophotes striking the boundary and boosts</div><div>the information propagation in an unbiased manner, in terms of pixel intensity values. Additionally, the low-cost, agility, and accessing flexibility benefits of cloud services have attracted user’s attention today. Besides, users are concerned about utilizing them for their data, as they are supported by untrusted third parties. Addressing these privacy concerns for object-removal in an image over the cloud server, we extended and modified our algorithm to make it compatible for (T; N)-threshold Shamir secret sharing scheme (SSS). This privacy-preserving system is an end-to-end system for object-removal in the ED over the cloud server namely Crypt-OR. Crypt-OR is evaluated by removing synthetically imposed objects in real-images. Further, Crypt-OR has proved to be secure under various pixel-based cryptographic attacks such as frequency-known attack and pixel-correlation attack. </div>

Vectorized linear approximations for attacks on SNOW 3G

SNOW 3G is a stream cipher designed in 2006 by ETSI/SAGE, serving in 3GPP as one of the standard algorithms for data confidentiality and integrity protection. It is also included in the 4G LTE standard. In this paper we derive vectorized linear approximations of the finite state machine in SNOW3G. In particular,we show one 24-bit approximation with a bias around 2−37 and one byte-oriented approximation with a bias around 2−40. We then use the approximations to launch attacks on SNOW 3G. The first approximation is used in a distinguishing attack resulting in an expected complexity of 2172 and the second one can be used in a standard fast correlation attack resulting in key recovery in an expected complexity of 2177. If the key length in SNOW 3G would be increased to 256 bits, the results show that there are then academic attacks on such a version faster than the exhaustive key search.

Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity

Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation fcorr attack.” Mitigating that in this paper, we propose an improved solution “c,k-anonymization” algorithm. The proposed solution thwarts the fcorr attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.